Lehigh’s Blacksmithing Club to open its first dedicated lab

Lehigh University’s Blacksmithing Club is set to move into its first dedicated lab space in Whitaker Laboratory, marking a major step for its growing community of student metalworkers. The club, supported by professor Laura Moyer and partnerships with Historic Bethlehem Museums & Sites and Lehigh Heavy Forge, offers hands-on experience in both traditional and modern metalworking. Students have also practiced basic techniques at the historic 1750 Smithy, while The Loewy Institute continues to provide education in advanced metal-forming technology.

Moyer said the team had discussed building a lab for years but wasn’t sure how to achieve it financially or where it would be located. 

With student interest rising, Moyer said the idea for the blacksmithing club started to solidify two years ago when a student — now club president Josh Swavely, ‘26 — mentioned his passion for blacksmithing in one of Misiolek’s classes.

Under the guidance of Moyer and Misiolek, the group helped launch a one-credit blacksmithing elective offered each spring. Moyer said this course, now in its second year, is open to students of all majors, as is the club. 

Moyer also said they are hoping to grow the one-credit course that is currently offered for half a semester into a full-semester three-credit elective for students. 

“Within the course we are developing, the idea is to balance time between the laboratory and the lecture hall, between practice and theory,” Misiolek said. “It will be much more of an opportunity to design your own products, analyze the proposed processes, and learn while you are going through the process.”

Moyer said the new laboratory will allow students to complete every stage of the design cycle in one place — from heating and shaping metal at the forge to examining the microstructure of their finished work.

She said students will also be able to fabricate hooks, blades and small hardware entirely on campus supported by new ventilation hoods, anvils and space for larger tools.

Misiolek said the club’s growth has been driven by access to working forges across Bethlehem. Through its partnership with Historic Bethlehem Museums & Sites, students learn centuries-old techniques at the 1750 Smithy.

He also said their work with the Lehigh Heavy Forge in Bethlehem allows students to witness a modern industrial facility that provides advanced forgings to clients.

“We are connecting the historical aspects of forging technology with hands-on experience and current industrial practices,” Misiolek said. 

He also said beyond traditional forging skills, the new lab will support lessons on heat treatments, controlled cooling and other methods that help students compare how different variables affect the strength and performance of their designs.

Nick Rockwell, a researcher at the Loewy Institute, said community partnerships have been essential in turning the club’s ideas into practice. 

“With all the support we’ve received, it’s really helped us connect with Historic Bethlehem,” he said. 

He also said Mike Rex, the shop and laboratory operations supervisor, has been instrumental in building facilities for the lab and giving advice on how to best use the space.

The club also gives students opportunities beyond the classroom. Members have participated in the Forging Industry Educational and Research Foundation’s annual competition and secured funding from the nonprofit organization to support their work.

“We pride ourselves in our hands-on approach,” Moyer said. “There are a lot of materials science and engineering departments across the country, but many don’t allow undergraduate students into labs or use highly specialized equipment.”

For more information: Lehigh University

Image: Josh Swavely, ’26, is pictured forging a point in the soon to be blacksmithing lab. Swavely is the president of the Lehigh Blacksmithing Club. (Max Randall/B&W Staff)

Using ultrabright X-rays to test materials for ultrafast aircraft

Designing hypersonic aircraft that travel at five to seven times the speed of sound is a major challenge because their materials must be lightweight yet capable of withstanding extreme heat and pressure. To address this, researchers at Embry-Riddle Aeronautical University, in collaboration with the U.S. Department of Energy’s Argonne National Laboratory, are developing a device that simulates the intense thermal and mechanical stresses of hypersonic flight. Paired with the ultrabright X-rays of Argonne’s Advanced Photon Source, this system will allow scientists to observe real-time changes in these materials under flight-like conditions.

“Recreating the environment of hypersonic flight can be complicated,” said Seetha Raghavan, professor of aerospace engineering and a co-principal investigator on the project. ​“There are so many factors and no perfect way to test them all. High enthalpy wind tunnels that can simulate the wind speed use a lot of energy resources and are limited in access.” (Enthalpy refers to the heat content of a system at constant pressure.)

The research team’s goal is an alternative that replicates hypersonic flight conditions using fewer energy resources and uses APS X-rays to capture detailed data. The APS is in the final stages of an upgrade that increased the brightness of its X-ray beams by up to 500 times. It is now the brightest synchrotron X-ray facility in the world, and according to Raghavan, the enhanced capabilities of the upgraded APS are crucial to this project.

“When you are talking about hypersonics, you’re talking about high speeds and fast changes, and response time is critical,” she said. ​“You can only get that kind of time resolution with enough flux (or brightness of the beam), and the upgraded APS is able to help with that.

“Additionally, the materials we’ll be testing are the thinnest that can be used, and at the upgraded APS you can focus the beam down to a small enough size to capture the data we need,” she said.

Victoria Cooley, an APS beamline scientist who worked with the Embry-Riddle team at beamline 1-ID, touted both the upgraded X-ray beam and the improved experiment station.

“It’s an exciting time for our beamline,” she said. ​“Brighter X-rays allow us to probe deep into materials with a higher-resolution beam and map very thin samples like these. At the same time, we have installed faster, more sensitive detectors to capture chemical or crystallographic changes occurring incredibly quickly. These two pieces come together to make world-changing projects such as this one possible.”

Uncovering materials that can withstand the conditions of hypersonic flight and are not prohibitively expensive to produce is key to unlocking their many applications. Durable hypersonic materials could be used for military and civilian aircraft, as well as cargo delivery vehicles.

The hypersonic materials project is supported by a $1.4 million contract from the U.S. Department of War Joint Hypersonics Transition Office through the University Consortium for Applied Hypersonics. The Embry-Riddle team’s principal investigator is William Engblom, professor of aerospace engineering, and Mark Ricklick, associate professor of aerospace engineering, is a co-principal investigator.

For more information: Embry-Riddle

Image: The Embry-Riddle research team at Beamline 1-ID, with Argonne scientist Victoria Cooley (back row, right). (Image by Mark Lopez/Argonne National Laboratory.)

Mapping the future: AI method to transform alloy properties prediction and design

Researchers at The Grainger College of Engineering have integrated their expertise in metals with advanced machine learning to create detailed spatial maps, enabling faster and more precise autonomous material design. Similar to how fingerprint technology captures intricate ridge and valley patterns for biometric identification, their approach leverages spatial mapping of fine details to revolutionize material engineering.

This evolution of recognition technology is mirrored in the field of materials science, where researchers seek new and efficient ways to fully characterize materials, accelerating the discovery of additional new materials. Much like human fingerprints, the performance of metal mixtures called alloys relies on the intricate spatial arrangement of microstructural features. Traditional methods reduce this complexity into a handful of averaged values, causing each alloy to lose its distinctive “fingerprint.”

 In a recent complement of papers from the lab of Jean-Charles Stinville, assistant professor of materials science and engineering, Illinois Grainger engineers have introduced new machine learning approaches for identifying alloy microstructures and predicting their properties rapidly. The Illinois researchers’ method will provide new avenues for faster and more efficient materials design. 

Microstructures are tiny structural features of metals that influence their strength and behavior. Scientists look to the microstructural properties of metals to assess their functionality. Metals used in propulsion devices like rockets and airplanes have special requirements. 

“We are sending these materials into increasingly extreme environments,” Stinville said. “They are exposed to intense environments; for instance, structural materials for space applications must be resistant to mechanical loading under extremely low or high temperatures. Conventional alloys don’t do as well in these conditions because their mechanical properties tend to degrade under these extreme environments. We want to find new ways to accelerate the identification of alloy chemistries and microstructures that can withstand these harsh conditions.”

The complete details of these microstructures, including small-scale influential variances called heterogeneities, cannot be easily captured by existing methods. Instead, Stinville and his colleagues used deep learning to analyze diffraction patterns, or the way electrons interact with metals. By encoding these interactions through a machine learning method onto a spatial latent representation, the researchers captured the full extent of an alloy’s microstructure and its heterogeneity — an approach Stinville calls Material Spatial Intelligence.

“Traditionally, we have used single descriptors or average values to guide data-based alloy design,” he said. “But spatial information from local measurements over a large field of view allows us to capture microstructure heterogeneity of the alloy. Using such spatial information in a data-based model provides significant improvement in prediction accuracy and enables alloy and microstructure design.” 

The initial model is a machine learning approach that successfully identified microstructures and material heterogeneity in unprecedented detail. In a second paper published in Scripta Materialia, Stinville further progressed the model towards the prediction of mechanical properties using the developed approach of material spatial intelligence. This method accelerates alloy property prediction by orders of magnitude and provides a rapid fundamental understanding of structure properties in metals. 

“I started my career as an experimentalist, where I developed tools that allowed us to collect large fields of view with very high resolution,” he said. “Then I went over to the numerical side to develop machine learning tools to actually use all this spatial information. As a metallurgist, I have an understanding that metals are controlled by local properties and their heterogeneities. My unique material scientist background really helped me in developing these novel models.”

By combining high-resolution digital image correlation with alloy microstructure characterization, Stinville examined tiny regions of metal surfaces and how they deformed at a small scale when loaded. Training a new model to recognize these deformation fingerprints allowed him to reliably predict important properties like strength, fatigue life, and ductility (the ability to extend without breaking). The model significantly decreases the time for testing, lessening the time needed to evaluate new alloys. This acceleration brings the field one step closer to intelligent alloy design.

Stinville envisions a future model that works backwards from a user’s desired properties to suggest a chemical composition and microstructure that best suits the given parameters. By integrating these approaches with his group’s advances in automated characterization, Stinville’s lab is setting the stage for fully autonomous alloy design, marking their next frontier.

But even as exciting advancements loom, Stinville still marvels at his field’s early beginnings. 

“This approach unites our field’s fundamental understanding of metals with new and efficient AI database tools,” he said. “We’re not just taking these new tools and leaving behind what we’ve already learned. We’re integrating the present with the past.” 

Mathieu Calvat, Chris Bean and Dhruv Anjaria significantly contributed to this research.

For more information: NPJ Computational Materials

Image: Electron backscatter diffraction (EBSD) maps of the investigated Inconel 718 alloys. Inverse pole figure (IPF) maps along the X direction (horizontal) are presented for a A wrought and fully recrystallized 718 alloy, and a B additively manufactured as-built 718 alloy

Study shows light can reshape atom-thin semiconductors for next-generation optical devices

Rice University researchers have discovered that light can induce a physical shift in the atomic lattice of transition metal dichalcogenides (TMDs), a class of atom-thin semiconductors. This tunable effect, observed in a Janus-type TMD, opens the door to technologies that use light instead of electricity, enabling faster, cooler computer chips, ultrasensitive sensors, and flexible optoelectronic devices.

“In nonlinear optics, light can be reshaped to create new colors, faster pulses or optical switches that turn signals on and off,” said Kunyan Zhang, a Rice doctoral alumna who is a first author on a study documenting the effect. “Two-dimensional materials, which are only a few atoms thick, make it possible to build these optical tools on a very small scale.”

TMDs are layered crystals made of a transition metal such as molybdenum and two layers of a chalcogen element like sulfur or selenium. Their combination of electrical conductivity, light absorption and mechanical flexibility has made them one of the most versatile classes of materials for next-generation electronics and optoelectronics.

Within this family, Janus materials stand out for their asymmetry — an idea reflected in their name. Like their mythological namesake, these materials have two different faces: The top and bottom atoms are made of different chemical species, creating an internal imbalance that gives the crystal a built-in electrical polarity, making it particularly sensitive to light and external forces.

“Our work explores how the structure of Janus materials affects their optical behavior and how light itself can generate a force in the materials,” Zhang said.

Using laser light of different colors, the team studied how a two-layer Janus TMD material — molybdenum sulfur selenide stacked on molybdenum disulfide — converts light through a process called second harmonic generation (SHG), in which the material emits light at twice the frequency of the incoming beam. They found that when the incoming light matched the material’s natural resonances, the doubled-frequency light pattern became distorted, signaling that the atoms inside were being displaced.

“We discovered that shining light on Janus molybdenum sulfur selenide and molybdenum disulfide creates tiny, directional forces inside the material, which show up as changes in its SHG pattern,” Zhang said. “Normally, the SHG signal forms a six-pointed ‘flower’ shape that mirrors the crystal’s symmetry. But when light pushes on the atoms, this symmetry breaks — the petals of the pattern shrink unevenly.”

The team traced the distortion to optostriction, a process in which the electromagnetic field of light itself exerts a mechanical push on atoms. In Janus materials, that push is amplified by strong coupling between the atomic layers, allowing even minute forces to produce measurable strain.

“Janus materials are ideal for this because their uneven composition creates an enhanced coupling between layers, which makes them more sensitive to light’s tiny forces — forces so small that it is difficult to measure directly, but we can detect them through changes in the SHG signal pattern,” Zhang said.

That sensitivity could make these materials useful far beyond the lab. Components that switch or route light using this principle could make optical chips faster and far more energy-efficient, since light-based circuits generate less heat than conventional electronics. The same responsiveness could be harnessed to create precise sensors capable of detecting the smallest vibrations or pressure changes or tunable light sources for advanced displays and imaging tools.

“Such active control could help design next-generation photonic chips, ultrasensitive detectors or quantum light sources ⎯ technologies that use light to carry and process information instead of relying on electricity,” said Shengxi Huang, an associate professor of electrical and computer engineering and materials science and nanoengineering at Rice and a corresponding author on the study. Huang is also a member of the Smalley-Curl Institute, the Rice Advanced Materials Institute and the Ken Kennedy Institute at Rice.

By showing how Janus TMDs’ built-in imbalance opens new ways to steer the flow of light, the study highlights how small structural features can unlock large technological potential.

For more information: ACS Nano

Image: Shengxi Huang is an associate professor of electrical and computer engineering and materials science and nanoengineering at Rice University. (Photo by Jeff Fitlow/Rice University)

Argonne-led Q-NEXT quantum center renewed for five years

The U.S. Department of Energy (DOE) has renewed funding for Q-NEXT, a National Quantum Information Science Research Center led by Argonne National Laboratory with SLAC National Accelerator Laboratory, for another five years. Backed by $125 million—$25 million in fiscal year 2026 and future funding subject to congressional approval—this investment ensures Q-NEXT will continue driving advancements in quantum information science and technology, reinforcing the United States’ leadership in this transformative field.

“Quantum information science is a cornerstone of the nation’s technological future, with the potential to transform industries including computing, healthcare and national security,” said inaugural Q-NEXT Director David Awschalom, who is also a senior scientist at Argonne, the director of quantum engineering at the University of Chicago Pritzker School of Molecular Engineering, and director of the Chicago Quantum Exchange. ​“Through our renewed mission, Q-NEXT will continue to push the boundaries of what is possible in quantum science, delivering the foundational knowledge, tools and technologies needed to ensure national leadership in this critical field.”

Awschalom has now assumed the role of chief science officer.

Q-NEXT’s mission is to unlock the future of quantum information by seamlessly integrating quantum and traditional information systems across optical networks. Building on its achievements over the past five years, the center will focus on demonstrating the potential of distributed quantum entanglement — a phenomenon where qubits, the fundamental unit of quantum information, remain connected even when separated by large distances.

“We’re building on the strong foundation we’ve laid over the past five years to take on our renewed mission — harnessing distributed entanglement to show what’s possible with scalable quantum platforms,” said Q-NEXT Director and Argonne scientist Martin Holt. ​“By uniting quantum technologies across optical networks, we will pave the way for systems capable of revolutionizing how we process, transmit and receive information.”

Q-NEXT brings together a strong network of partners: two DOE national laboratories, 11 leading universities and six tech companies. This cross-sector collaboration ensures that the center’s work is both innovative and practical, bridging the gap between scientific discovery and real-world application. Universities contribute expertise in quantum sensing and communication, while industry partners provide access to state-of-the-art prototypes and manufacturing capabilities. Together, Q-NEXT partners form a vibrant ecosystem that drives progress in quantum science.

“We envision quantum systems that work across chip-to-chip, lab-to-lab and city-to-city scales,” said Q-NEXT Deputy Director Jennifer Dionne, who is a professor of materials science and engineering and, by courtesy, of radiology at Stanford University and SLAC. ​“We’re excited to advance a shared set of hardware, software and protocols to make quantum networks and sensors efficient and practical.”

Q-NEXT’s renewed efforts will focus on three scientific goals:

  1. Communication — developing quantum communication networks that link devices across metropolitan areas. Q-NEXT aims to demonstrate algorithms that run across multiple remote, connected quantum processors.
  2. Sensing — using quantum entanglement to achieve unprecedented precision in sensing applications. Q-NEXT will demonstrate real-world uses, such as in medicine and navigation, as well as foundational scientific discoveries in gravitation and quantum mechanics where quantum entanglement gives a clear advantage in sensing and measurement.
  3. Materials — developing new approaches to integrating materials that can be scaled for industry use. Q-NEXT will tackle the most important challenges involved in merging distinct quantum materials systems with advanced functionality and integrating them into practical quantum devices.

Q-NEXT will pursue its goals through large-scale, team-based projects that combine materials science, device engineering and quantum physics theory. By leveraging world-class science facilities, Q-NEXT aims to deliver breakthroughs that will shape the future of quantum technology. These facilities include the Argonne and SLAC Quantum Foundries, the Stanford Synchrotron Radiation Lightsource and several DOE Office of Science user facilities: Argonne’s Advanced Photon Source and Center for Nanoscale Materials, the Argonne Leadership Computing Facility and SLAC’s Linac Coherent Light Source.

In addition to its scientific mission, Q-NEXT will continue to build the next generation of quantum scientists, engineers, technicians and other professionals. Through educational programs, internships and training opportunities such as the DOE Science Undergraduate Laboratory Internship and the Open Quantum Initiative Undergraduate Fellowship, the center is preparing students, early-career researchers and operations specialists to thrive in the rapidly growing quantum industry.

“By fostering a skilled workforce, Q-NEXT is ensuring that the U.S. remains at the forefront of quantum innovation,” Awschalom said.

Q-NEXT was established in 2020, and in its first five years, it led the establishment of the Argonne and SLAC Quantum Foundries. Together these two national facilities contribute to a robust supply chain of standardized materials and devices.

“We’ve built an active, cross-disciplinary collaboration that’s laid the groundwork for networked quantum information by intertwining computing, sensing and communication,” Holt said. ​“Over the next five years, we’ll strengthen these efforts, strategically coordinating with the other NQISRCs and the national quantum ecosystem to accelerate the arrival of transformative quantum technologies.”

Collaboration with industry has been essential to the center’s mission — turning cutting-edge research into innovations that can transform technology and strengthen the nation’s quantum leadership.

“IBM’s work with the National Quantum Information Science Research Centers, such as Q-NEXT led by Argonne, is vital to our mission to build the future of computing,” said Jay Gambetta, director of IBM Research and IBM Fellow. IBM is a Q-NEXT partner. ​“Together, we are exploring how efficient quantum networks can be created through optical links connected to IBM’s quantum networking units. This could deliver the fundamental technology needed to link the multiple, interconnected fault-tolerant quantum computers of the future over kilometer distances, allowing the convergence of quantum computation and communication within a future quantum computing internet that could revolutionize scientific and industrial discovery.”

Q-NEXT is one of five DOE National Quantum Information Science Research Centers renewed for another five years.

“Quantum technologies are driving innovations across society, and our laboratory is focusing on delivering science breakthroughs to accelerate these innovations,” said Argonne Director Paul Kearns. ​“With a renewed Q-NEXT, we will continue to play an integral role in the national quantum ecosystem through coordinated, complementary efforts with DOE’s other quantum research centers. We are committed to realizing the promise of quantum information to build a more connected world and shape a future of scientific progress that strengthens our nation’s security, prosperity and technological leadership.”

For more information: Argonne National Laboratory

Image: The DOE has renewed the Q-NEXT quantum center for five years with a $125 million investment. One of Q-NEXT’s major first-run accomplishments was the establishment of two national quantum foundries — one at Argonne (pictured) and one at SLAC. (Image by Argonne National Laboratory.)

Scientists turn common semiconductor into a superconductor

Researchers have long sought to make semiconductors—essential for computer chips and solar cells—function as superconductors, which can carry electricity without resistance for faster, more efficient performance. This has been difficult because it requires a precise atomic structure that enables free electron movement. Now, an international team has achieved a breakthrough by creating a form of germanium that exhibits superconductivity, allowing electric currents to flow indefinitely without energy loss. This advancement could significantly enhance electronic and quantum devices while reducing power consumption.

“Establishing superconductivity in germanium, which is already widely used in computer chips and fiber optics, can potentially revolutionize scores of consumer products and industrial technologies,” explains Javad Shabani, a physicist at New York University and director of its Center of Quantum Information Physics and Quantum Institute.

Peter Jacobson, a physicist at the University of Queensland, adds that the findings could accelerate progress in building practical quantum systems. “These materials could underpin future quantum circuits, sensors, and low-power cryogenic electronics, all of which need clean interfaces between superconducting and semiconducting regions,” he says. “Germanium is already a workhorse material for advanced semiconductor technologies, so by showing it can also become superconducting under controlled growth conditions there’s now potential for scalable, foundry-ready quantum devices.”

Germanium and silicon, both group IV elements with diamond-like crystal structures, occupy a unique position between metals and insulators. Their versatility and durability make them central to modern manufacturing. To induce superconductivity in such elements, scientists must carefully alter their atomic structure to increase the number of electrons available for conduction. These electrons then pair up and move through the material without resistance — a process that is notoriously difficult to fine-tune on the atomic scale.

In the new study, researchers developed germanium films heavily infused with gallium, a softer element commonly used in electronics. This technique, known as “doping,” has long been used to modify a semiconductor’s electrical behavior. Normally, high levels of gallium destabilize the crystal, preventing superconductivity.

The team overcame this limitation using advanced X-ray methods to guide a refined process that encourages gallium atoms to take the place of germanium atoms in the crystal lattice. Although this substitution slightly distorts the crystal, it preserves its overall stability and allows it to carry current with zero resistance at 3.5 Kelvin (about -453 degrees Fahrenheit), confirming that it had become superconducting.

“Rather than ion implantation, molecular beam epitaxy was used to precisely incorporate gallium atoms into the germanium’s crystal lattice,” says Julian Steele, a physicist at the University of Queensland and a co-author of the study. “Using epitaxy — growing thin crystal layers — means we can finally achieve the structural precision needed to understand and control how superconductivity emerges in these materials.”

As Shabani notes, “This works because group IV elements don’t naturally superconduct under normal conditions, but modifying their crystal structure enables the formation of electron pairings that allow superconductivity.”

For more information: Nature Nanotechnology

Image: Josephson junction structures—quantum devices made of two superconductors and a thin non-superconducting barrier—using different forms of germanium (Ge): super-Ge (in gold), semiconducting Ge (in blue), and super-Ge on wafer-level scale. Millions of Josephson junction pixels (10 micrometer square) can be created with this new material stack on wafer scale. Inset shows crystalline form of Super-Ge on the same matrix of semiconductor Ge, a key for crystalline Josephson junction. Credit: Patrick Strohbeen/NYU

Physics-based machine learning could unlock better 3D-printed materials

An NSF-funded initiative led by Lehigh University’s Parisa Khodabakhshi is advancing machine learning models that integrate physical laws to accelerate the design of high-performance alloys for aerospace, automotive, and healthcare applications. This effort complements additive manufacturing—also known as 3D printing—which constructs objects layer by layer using materials like metals, polymers, and biomaterials, offering a powerful combination for next-generation engineering solutions.

“This layer-by-layer approach allows for the fabrication of parts with complex geometries that are often difficult, or even impossible, to achieve with conventional manufacturing methods,” says Parisa Khodabakhshi, an assistant professor of Mechanical Engineering and Mechanics at Lehigh University’s P.C. Rossin College of Engineering and Applied Science. “However, the thermomechanical properties of the final additively manufactured parts are influenced by a large number of process parameters, making design optimization particularly challenging.”

Establishing the map between variations in process parameters and the final part’s properties requires several simulations across a wide range of length scales, making the task computationally expensive. “The computational demands of performing all the necessary simulations make it impractical,” says Khodabakhshi. As a result, manufacturers often resort to trial-and-error methods to achieve desired thermal or mechanical properties in the end product.

“However, you cannot fully explore the entire design space that way to find the optimal design, which is why we’re currently not able to utilize the full potential of additive manufacturing.”

Khodabakhshi recently received a three-year, $350,000 grant from the National Science Foundation to develop a computationally efficient model that accurately predicts how additive manufacturing process parameters influence the solidification microstructure, which in turn determines the properties of the final part. Specifically, Khodabakhshi will develop a physics-based, data-driven reduced-order model for predicting microstructure evolution in binary alloy solidification (or when a mixture of two metals changes from liquid to solid). 

“For example, say I want a part that has specific thermal properties,” she says. “I don’t know what my process parameters should be to achieve those properties. The simulations that link given process parameters to the resulting solidification microstructure, and consequently the final properties of the built part, are highly nonlinear. We refer to this simulation as the forward map. From there, I can construct the inverse map, which connects desired properties back to the process parameters.” The NSF project focuses on developing a computationally efficient model for the process-structure (PS) relationship.

The ultimate goal is to optimize the manufacturing of additively manufactured parts, which are especially useful in the aerospace, automotive, and healthcare industries. Fields in which confidence in manufacturing is paramount.

Her team’s approach uses a scientific machine learning framework that blends data-driven machine learning algorithms with physical laws. 

“As engineers, we don’t want to just train a black-box algorithm,” says Khodabakhshi. “We want to embed physics into the problem to satisfy the governing equations of the physical phenomena so that we’re confident about the output that we receive from the from the algorithm. That’s the difference between conventional machine learning and scientific machine learning.”

For more information: Lehigh University

Image: Parisa Khodabakhshi, assistant professor, mechanical engineering and mechanics

New model reveals the hidden structure of everyday materials

Scientists are working to better understand how components in mixed materials like concrete or underground rock are distributed, which could lead to stronger materials and safer storage of substances like carbon dioxide or nuclear waste. A key tool in this effort is the Poisson model, which randomly divides space using flat surfaces to simulate how materials mix. Although useful in fields like radiation transport, the model lacked precise formulas to describe relationships between multiple points—limiting its accuracy and application in complex systems until recent breakthroughs addressed this gap.

In a new study, Stanford researchers introduced a clever math method that helps reveal what a material is made of, just by knowing details from one random spot. They used a well-known statistical model, called the Poisson model, which randomly divides space, to study materials such as sand and concrete. This new approach enables scientists to understand the tiny structure of these materials with very high accuracy, which could aid in designing stronger and more reliable materials.

Lead study author Alec Shelley said, “With this study, we’ve solved the famous Poisson model for heterogenous materials.”

“Our result could have a broad impact on several areas of science, because heterogenous materials are common and their models almost never have exact solutions.”

The tiny structure inside materials affects how strong, durable, and useful they are. Thanks to the new research, scientists can now understand these structures more precisely.

“What Alec has succeeded in doing in this study is quite remarkable,” said Daniel Tartakovsky, a professor of energy science and engineering. “Using his approach, you could design a composite material to your specifications and obtain certain properties based on the proper mixture of components.”

Shelley and Tartakovsky plan to use their new math method to predict what different materials are made of. Their model can reveal a long list of important properties that depend on a material’s tiny inner structure, such as hardness and elasticity, tensile strength (how much it can stretch before breaking), electrical and heat conductivity, how fast one substance moves through another, magnetic behavior, and how much light passes through.

Concrete has tiny air pockets inside it. If engineers can accurately model these spaces, they could use materials like fly ash, slag, or biochar to fill them in. This would reduce the amount of cement needed, helping to lower carbon dioxide emissions from cement production, while also making the concrete stronger and more affordable.

Additional applications include modeling fractured and porous media, a central challenge in groundwater management, as well as in nuclear waste disposal, geothermal energy, and carbon sequestration.

“These systems are complex and difficult to model,” said Tartakovsky. “However, the Poisson model’s multipoint functions that we solve in this study offer a new tool for understanding and predicting their behavior.”

In this way, as a microstructural model, the Poisson model can accurately simulate a wide range of heterogenous materials, including everything from the appearance and distribution of ice fragments on a frozen lake to the marbling in a juicy steak.

Shelley shared a neat way to understand the Poisson model. Imagine taking a blank piece of paper and randomly drawing lines across it to create different sections. Then, color each section however you want, it’s like creating a colorful mosaic! The new research takes this idea a step further by picturing another piece of paper laid over that mosaic.

If you poke a hole in the top sheet, you see one color below. That small peek gives a clue about the whole pattern. By making more holes and using a math method called multipoint correlations, you can predict the full design more accurately each time. This approach mirrors how scientists study heterogeneous materials, such as concrete or rock, by using small samples to understand the bigger picture.

“It’s like we’ve created the perfect Battleship player for guessing colors in this model,” Shelley said.

To handle the intricate math involved in the Poisson model’s multipoint correlations, Shelley took a hands-on approach. He began by sketching ideas in a notebook to help picture the problem. Figuring out two points was simple, but things got complicated fast, by the time he worked on three points, he had to deal with 128 different terms.

By the time he reached the four-point scenarios, the complexity was overwhelming, pushing him to turn to computer simulations. It was a necessary shift that saved him from spending months buried in calculations. on manual work.

According to Shelley, the seemingly painstaking work was anything but. “I love math, and I was a math double major in undergrad, so I had the knowledge to go in and try this problem out,” he said.

For more information: Physical Review Letters

Scientists create a paper-thin light that glows like the sun

Scientists have created an ultra-thin, paper-like LED that emits a warm, sunlike glow by precisely blending red, yellow-green, and blue quantum dots to mimic natural sunlight. This breakthrough offers improved color accuracy and reduced eye strain, with potential applications in home lighting, electronic displays, and workspaces. Unlike traditional bulky bulbs, this slender design could transform how we light our environments while helping minimize sleep disruption from harsh artificial light.

“This work demonstrates the feasibility of ultra-thin, large-area quantum dot LEDs that closely match the solar spectrum,” says Xianghua Wang, a corresponding author of the study. “These devices could enable next-generation eye-friendly displays, adaptive indoor lighting, and even wavelength-tunable sources for horticulture or well-being applications.”

Many people prefer indoor lighting that feels natural and soothing. Earlier approaches achieved this effect with flexible LEDs that used red and yellow phosphorescent dyes to create a candle-like warmth. A newer alternative relies on quantum dots—tiny semiconductor particles that transform electrical energy into colored light. Some research teams have already used quantum dots to make white LEDs, but replicating the complete spectrum of sunlight has remained difficult, particularly in the yellow and green regions where sunlight is strongest. To address this challenge, Lei Chen and colleagues developed quantum dots that could recreate that balanced, sunlike glow in a thin, white quantum dot LED (QLED). Meanwhile, Wang’s group proposed an efficient conductive material design that could operate effectively at relatively low voltages.

The team began by synthesizing red, yellow-green, and blue quantum dots coated with zinc-sulfur shells. They determined the precise color ratio needed to match the spectrum of natural sunlight as closely as possible. Next, they assembled the QLED on an indium tin oxide glass substrate, layering conductive polymers, the quantum dot blend, metal oxide particles, and finally a top coating of aluminum or silver. The quantum dot layer measured only a few dozen nanometers in thickness—much thinner than standard color conversion layers—resulting in a white QLED with an overall profile comparable to wallpaper.

In initial tests, the thin QLED performed best under a 11.5-volt (V) power supply, giving off the maxmium bright, warm white light. The emitted light had more intensity in red wavelengths and less intensity in blue wavelengths, which is better for sleep and eye health, according to the researchers. Objects illuminated by the QLED should appear close to their true colors, scoring over 92% on the color rendering index.

In further experiments, the researchers made 26 white QLED devices, using the same quantum dots but different electrically conductive materials to optimize the operating voltage. These light sources required only 8 V to reach maximum light output, and about 80% exceeded the target brightness for computer monitors.

For more information: ACS Applied Materials

Image: A paper-thin device uses quantum dots, similar to those described in this work, to light up LEDs. Credit: Lin Zhou, Xianghua Wang

Rice researchers create novel metamaterial that can potentially revolutionize implantable, ingestible devices

A team led by Rice University’s Yong Lin Kong has developed a soft yet strong metamaterial capable of rapidly changing its size and shape through remote control, marking a major step forward for ingestible and implantable medical devices. Unlike natural materials, metamaterials derive their unique properties from their physical structure rather than chemical composition. Kong’s design combines exceptional deformability with structural stability—an unprecedented feat in soft materials—allowing it to withstand compressive loads over ten times its own weight and perform reliably in extreme temperatures and harsh chemical environments.

“We programmed multistability, i.e. the ability to exist in multiple stable states, into the soft structure by incorporating geometric features such as trapezoidal supporting segments and reinforced beams,” said Kong, assistant professor of mechanical engineering at Rice’s George R. Brown School of Engineering and Computing. “These elements create an energy barrier that locks the structure into its new shape even after the external actuation force is removed.”

The metamaterial’s soft architecture helps address critical medical safety concerns such as gastric ulcers, puncture injuries and inflammation that can occur from implantable and ingestible devices made of rigid components.

Kong and his team used 3D printing to create molds that form interconnected microarchitectures of tilted beams and supporting segments. This design allows for rapid switching between open (off) and closed (on) states, and the transformed configuration is maintained even after the magnetic field is removed. By combining many such unit cells as “building blocks,” they form a 3D structure that can not only transform its shape but can also produce complex peristaltic motions to move or to deliver fluids in a controlled manner when actuated with a magnetic field.

Importantly, the material continued to function reliably even after prolonged exposure to mechanical stress and acidic corrosion, conditions that mimic the harsh environment of the human stomach.

“The metamaterial makes it possible to remotely control the size and shape of devices inside the body. This could enable lifesaving capabilities such as precisely controlling where a device stays, delivering medication where it’s needed or applying targeted mechanical forces deep inside the body,” Kong said. “We are now leveraging this metamaterial to develop ingestible systems that may one day treat obesity in humans or improve the health of marine mammals, and we are collaborating with surgeons at the Texas Medical Center to design wireless fluidic control systems to address unmet clinical needs.”

The first author of this study was Kong’s first graduate student, Taylor Greenwood, who has since graduated and started a faculty position at Brigham Young University. Kong’s other graduate students Brian Elder and Jared Anklam, postdoctoral associates Jian Teng and Saebom Lee and other collaborators were involved in the study. This research was supported by the National Institutes of Health and the Office of Naval Research.

For more information: Science Advances

Image: The new metamaterial designed by Kong and his team at Rice can be controlled remotely to rapidly transform its size and shape (Photos and video by Jorge Vidal/Rice University).

US scientists bring quantum-level accuracy to molecular modeling, sharpen predictions

Researchers at the University of Michigan have developed a breakthrough method that brings quantum-level precision to molecular modeling, offering deeper insights into chemical reactions and material properties. This advancement addresses the quantum many-body problem—how electrons interact to form chemical bonds and influence electrical behavior—which traditionally requires immense computational power and is limited to small molecules. By enhancing the efficiency of this simulation approach, the new method could extend quantum accuracy to larger, more complex systems, potentially reducing the heavy demand on national lab supercomputers.

Density functional theory, or DFT, makes quantum chemistry more manageable by focusing on electron densities rather than tracking every electron individually. This approach keeps computing demands much lower, allowing simulations of systems with hundreds of atoms. A major challenge, however, lies in the exchange-correlation (XC) functional, which governs how electrons interact according to quantum mechanics. 

Until now, researchers have had to rely on approximations of the XC functional tailored to specific applications, limiting the theory’s overall accuracy. Improving this functional is key to making DFT an even more powerful tool for chemistry and materials science.

According to Vikram Gavini, a University of Michigan professor of mechanical engineering and the corresponding author of the study, researchers know that a universal functional exists that applies to all electron systems – whether in molecules, metals, or semiconductors – but its exact form remains unknown.

Hence, understanding this functional is crucial for improving DFT, which models electron interactions and underpins simulations in chemistry and materials science.

Given DFT’s central role in advancing both materials research and basic science, the US Department of Energy provided funding and supercomputer resources to support the University of Michigan team’s efforts to approach the universal exchange-correlation functional. 

The researchers began by analyzing individual atoms and small molecules using quantum many-body theory. Then, instead of applying approximate functionals to predict electron behavior, they used machine learning to determine which XC functional would reproduce the electrons’ behavior as calculated by the more precise quantum many-body method.

Bikash Kanungo, a University of Michigan assistant research scientist in mechanical engineering and first author of the study, explains that an accurate exchange-correlation functional has broad applications because it is material-agnostic.

It is equally important for researchers developing better battery materials, designing new drugs, or building quantum computers. By improving this functional, scientists can make density functional theory more reliable and widely applicable, enabling more precise simulations across chemistry, materials science, and emerging technologies.

Thus, researchers can now use the XC functional discovered by the University of Michigan team or apply their approach to new systems, starting with light atoms and molecules and eventually extending to solids, paving the way for more accurate and efficient simulations across chemistry and materials science.

For more information: University of Michigan

Image: A 3D map of the quantum potential.

AI-powered approach simplifies exploration of complex materials

Researchers at Oak Ridge National Laboratory have introduced a powerful new method for investigating the atomic-level behavior of materials by combining Bayesian deep learning—a fusion of probability theory and neural networks—with advanced data analysis. This approach enables scientists to rapidly and accurately process complex datasets, allowing them to scan broader sample areas and identify regions with critical properties far more efficiently than traditional techniques.

“This method makes it possible to study a material’s properties with much greater efficiency,” said Ganesh Narasimha from ORNL. “Usually, we would need to scan a large region, and then several small regions, and perform spectroscopy, which is very time-consuming. Here, the AI algorithm takes control and does this process automatically and intelligently.”

The team demonstrated the system using europium zinc arsenide, a magnetic semimetal with distinctive electronic traits. With the aid of scanning tunneling microscopy, the researchers uncovered links between atomic-scale structures and their electronic responses.

Although the case study focused on europium zinc arsenide, the scientists emphasize that the method is broadly applicable to many different materials. The advance not only streamlines the discovery process but also strengthens national efforts in artificial intelligence and quantum science.

For more information: Nature

Image: A scanning tunneling microscope and machine learning algorithm autonomously search for atomic structures. This image shows a vacancy defect on europium zinc arsenide. (Image Credit: Ganesh Narasimha/ORNL, U.S. Dept. of Energy)

Breakthrough phason discovery in twisted 2D materials transforms quantum computing

Scientists have directly imaged a rare atomic vibration known as a phason—a phenomenon long theorized but never observed—within a twisted two-dimensional material called tungsten diselenide. These ultra-thin materials, just a few atoms thick, can be stacked in unusual configurations that produce entirely new physical behaviors. Using a cutting-edge technique called electron ptychography, researchers captured the most precise images of individual atoms to date, revealing how phasons vary with atomic arrangement and opening new possibilities for future electronic technologies.

They are only a special case of a broader category of moiré phonons, the result of when two layers of 2D material are shifted slightly one from the other. That fold between the two creates a moiré superlattice—a larger periodic structure that gives rise to unique thermal and electronic behavior. Phasons are an extremely soft, low-frequency variety of these phonons.

Even though phonons and phasons are not seen, their effect is widespread. Heat is atomic vibration in itself, and an understanding of such patterns is required if one expects to have any control over heat transfer in electronics. Greater control could imply materials that cool faster or suck heat off sensitive parts.

You can’t just get rid of phasons; that’s the blessing and curse,” said Pinshane Huang, a professor of materials science and engineering at the University of Illinois at Urbana-Champaign and lead author of the paper. “They’ve always been hiding in plain sight, changing the properties of 2D moiré materials.”

Phasons had mystified scientists for decades. They were thought to belong in theoretical models only, but no one had concrete proof. Not until Huang teamed up with Yichao Zhang, a postdoctoral researcher of nanoscale heat conduction. Their task: to freeze the blur of atomic movement created by heat.

What our primary goal was, was to see heat when looking at an atom,” Huang explained. “What this does is get so fantastic spatial resolution that vibrations of the atoms have something to do with how fuzzy the atoms appear,” she said. “These are tiny movements, and we can basically look at one atom at a time and see its heat-related motion.”.

To do this, the team utilized electron ptychography, a new imaging method with a resolution of less than 15 picometers. That is about one-thousandth the width of an individual atom. It allowed the team to see the width, shape, and movement of atoms in twisted bilayer tungsten diselenide, or WSe₂.

“Back when I started out, we figured the highest resolution you could get was maybe a little bit less than an angstrom,” Huang said. “But once ptychography arrived on the scene, we were starting to see resolution numbers as low as 0.2 angstroms,” she continued. “That put us thinking, ‘Well, heat gets atoms jigged around about 0.05 angstroms,’ she said. “Now that we can actually see heat, it shows what a giant jump in resolution can do for what microscopes can do,” she continued.

Twisted structures are important because they alter the local atomic environment in strong and unexpected manners. When one layer is twisted against another in bilayer materials such as WSe₂, there is a mismatch in the grids of atoms. That mismatch creates new areas—some with atoms close together and others not so close.

When researchers mapped these helical regions, they found more vibrations in some areas. Vibrations were especially strong around solitons, where solitons are borders between different stacking modes. AA-stacked regions, in which atoms lie atop each other, also showed more intense vibrations.

By integrating electron ptychography with lattice dynamics and molecular dynamics simulations, the scientists made a robust conclusion. Phasons were discovered by them as the main culprit behind thermal vibration in low-angle twisted bilayers. This tells us that the moiré pattern is not just an illusion—it plays a pivotal role in determining how heat traverses through the material. These findings provide new doors to investigate and even control heat in 2D materials at the atomic level.

Phasons are tiny, but their potential effect could be huge. Controlling the skill of sensing and studying these vibrations could change the way electronics are designed in the future. “One potential application of this technique is creating materials that are better heat conductors,” said Zhang. “We can look at one atom and find a defect that keeps the material from cooling more efficiently,” he said.

This could result in better means of atomic-level thermal control,” he added. “To watch individual atoms and see how they react to heat will tell us some very important things,” he said.

Twisted 2D materials are already being researched for use in transistors, sensors, and quantum computing hardware. Adding heat behavior to that research means engineers can create entire devices at the atomic level. Such devices would be able to be made smaller, faster, and much more efficient than today’s electronics.

Perhaps most exciting is the manner in which this finding reconciles theory with direct observation. Phasons were previously hypothetical, known by equations and models alone. Now, they are observable in real time.

Using equipment like electron ptychography, even the slightest motion of the atoms can be watched and measured. Science no longer needs to speculate about what atoms do—it’s watching them in plain sight, one vibration at a time.

For more information: Science

Image: (left) atoms present in the 2D material. (right) photos of single atoms. (CREDIT: The Grainger College of Engineering)

How Argonne is helping to expand the Quantum Prairie

Silicon Valley may have led the digital revolution, but Illinois is emerging as a hub for quantum innovation, dubbed the “Quantum Prairie.” Anchored by institutions like the U.S. Department of Energy’s Argonne National Laboratory, the region is attracting both tech giants and startups focused on quantum information science. Researchers are now harnessing quantum physics—first discovered over a century ago—to revolutionize fields such as computing, medicine, and finance, using atomic-scale phenomena to build ultra-sensitive sensors and simulate complex physical systems.

“The pace of scientific discovery is truly remarkable, and the levels of global engagement have dramatically increased,” said David Awschalom, the Quantum Information Science and Technology group leader at Argonne, professor at the University of Chicago and director of Q-NEXT, a DOE National Quantum Information Science Research Center led by Argonne. “Quantum science is rapidly becoming a technology that will have both economic and security implications.”
Argonne is accelerating quantum information science in multiple ways, including creating materials for qubits, which are units of quantum data, and helping to build testbeds for quantum technology such as ARQNET, InterQnet, the Chicago Quantum Network and the Chicago Quantum Computing Testbed. At Argonne, the upgraded Advanced Photon Source (APS), the Center for Nanoscale Materials and the Argonne Leadership Computing Facility (ALCF) — all DOE Office of Science user facilities — have enabled such breakthroughs. In 2023, the Argonne Quantum Foundry was launched to develop scalable semiconductor quantum systems.

“Building quantum information systems requires new materials, different computing architectures, specialized devices and software. You need to look at how quantum computers will communicate with one another and also how they will connect to classical computer systems,” said Argonne Distinguished Fellow Michael Norman, director of the Argonne Quantum Institute. “At Argonne, we’re doing all of it.”

But the lab’s homegrown capabilities are only part of its role as a key player in a growing Midwestern quantum ecosystem. Through Q-NEXT, Argonne spearheads collaboration on quantum technologies. It is a founding member of both the Chicago Quantum Exchange, a University of Chicago-based community of more than 50 corporate, international, nonprofit and regional partners that launched and nurtured the Illinois-Wisconsin-Indiana quantum ecosystem, and Duality, the nation’s first quantum startup accelerator.

Argonne is also a key part of two federal designations: the Bloch Quantum Tech Hub, which was designated by the U.S. Economic Development Administration, and Quantum Connected, a Midwest coalition that is a National Science Foundation Regional Innovation Engine (NSF Engines) Development Awardee and a semifinalist in the national NSF Engines competition. And Argonne Laboratory Director Paul Kearns is on the Board of Managers for the Illinois Quantum and Microelectronics Park (IQMP), an in-progress campus in Chicago for quantum technology and microelectronics innovation.

The allure of Illinois for quantum tech
Building on this foundation, Illinois has announced multiple quantum technology initiatives and partnerships in an effort to make Illinois a global quantum capital. In 2024, the state and IBM announced the National Quantum Algorithm Center, which will be located at the IQMP, and quantum computing company PsiQuantum has signed on as the anchor IQMP tenant.

IQMP Executive Director and CEO Harley Johnson, who is also a professor of mechanical science and engineering at the University of Illinois Urbana-Champaign, points to the state’s research bona fides: top schools such as his employer, along with the University of Chicago and Northwestern University; two DOE national labs, Argonne and Fermi National Accelerator Laboratory, which also head up two of the five DOE National Quantum Information Science Research Centers; and a partnership with the U.S. Department of Defense, part of the larger Quantum Benchmarking Initiative (QBI), to be a “national proving ground” for quantum. Argonne is leading several of the teams vetting quantum computing companies for the QBI.

“That’s an incredible base to build on,” Johnson said. “Then, when you add the state government, nonprofits, economic development organizations and the companies that are already here, there’s a strong ‘team Illinois’ environment that comes across to potential partners. They see that we’re pulling in the same direction.”

Preeti Chalsani, chief quantum officer at the public-private economic development partnership Illinois Economic Development Corporation, said that when she participates in conferences, she hears talk of other states that want to replicate Illinois’ success in kickstarting the quantum industry.

“It’s not accidental that quantum is strong here,” Chalsani said, pointing to the state’s storied, multidisciplinary research history and assets such as Argonne’s APS and the ALCF, with its Aurora exascale computer. “Chicago and Illinois have done a really good job making researchers aware of quantum as not just a research enterprise, but a whole industry with lots of opportunities to collaborate.”

An ecosystem for quantum
The breadth of Argonne’s work in quantum information science, combined with the field’s increasing relevance, led the laboratory to create the Argonne Quantum Institute in October 2023. The institute coordinates research efforts both internally at the lab and with external partners, such as IBM, Infleqtion, Intel, Quantinuum and JPMorgan Chase. A recent article in Nature showcases the work of the latter two companies with Argonne scientists. Several scientists at the UChicago Pritzker School of Molecular Engineering, which has a core focus on quantum information research, conduct their work jointly at Argonne. The two institutions share several quantum-focused research initiatives as well.

The institute organizes quantum information research across four themes: computing and simulations; matter and materials; communications and networking; and quantum detecting and sensing. Recently, Argonne researchers developed an approach for controlling the collective magnetic properties of atoms in real time, which could be useful in quantum computers. Others are building qubits known as color centers at the Argonne Quantum Foundry.

“The fact that Argonne invests in the Quantum Foundry as a way to atomically engineer and construct materials has taken us from relying on external sources to provide materials over periods of months to performing everything inhouse within a day,” Awschalom said.

Some of the scientists working at Argonne are entrepreneurs who are building quantum companies with support from the lab’s two-year fellowship program, Chain Reaction Innovations. Founders of startups including memQ and Super.tech are Chain Reaction Innovations alumni. Quantum accelerator Duality, based at the University of Chicago and with Argonne as a founding partner, also provides funding and expertise to accepted startups. Both programs help connect entrepreneurs to the region’s larger quantum community and industry base.

“A lot of the work of startups relies on connections to research labs,” Chalsani said. “It also depends on connections to larger companies who could be strategic partners that can help with their product development or go-to-market strategy, or customers who would adopt their technologies. This work really cannot be done in isolation.”

Building the quantum workforce
The key to a quantum future is a strong workforce. Argonne invests in the next generation of quantum scientists by hosting summer research experiences for undergraduates, who receive hands-on training at the Argonne Quantum Foundry. And Q-NEXT, for which Argonne is the lead laboratory, has supported more than 150 students and postdocs over the past five years.

These initiatives address the rapidly growing demand for professionals with quantum materials and computational expertise, reinforcing Argonne’s commitment to empowering future scientific leaders.

“Quantum science won’t advance without the next generation,” Norman said. “Giving students and early-career researchers real lab experience, good mentors and a solid foundation is how we’ll keep Argonne at the forefront of this field. Our efforts here have been enabled by both Chicago Quantum Exchange’s Open Quantum Initiative program and DOE’s Science Undergraduate Laboratory Internships.”

Even though quantum information science has been progressing steadily over the past decade, specialized graduate programs have only just begun to appear within the last five years or so. The University of Chicago introduced one of the first in the nation. The explosion of investment and innovation in quantum information science is creating demand for expertise.

“One of the biggest challenges in the field is not only the science and technology, it’s scaling the workforce at all levels to meet growing demand,” Awschalom said. “How do we create a sufficient supply of quantum engineers and technicians to innovate and build quantum technologies?”

The answer is not just people with advanced schooling. More than half of quantum technology jobs do not require a graduate degree.

“Several years ago, it was mostly research scientists with Ph.D.s or postdocs who were involved in quantum,” Chalsani said. “But as the industry has matured, it requires a whole range of other talent.” That includes equipment technicians, software engineers, HVAC installers and other key roles.

Some of the technologies this workforce will support are already here, such as quantum navigation sensors. Some, like quantum computing, are further off.

And others? We don’t know what they are yet.

“History has shown that whenever there are discontinuous changes in science and technology, the biggest impacts are the ones we’re not imagining in this conversation,” Awschalom said. “The trick is, will we be ready when they arrive? Because I guarantee you, they’re going to appear — they always do.”

For more information: Argonne National Laboratory
Image: Argonne researchers are developing technologies and protocols to enable scalable, long-distance quantum communication. (Image by Argonne National Laboratory.)

Enabling an electric future, researchers create electrode-agnostic electrolyte

Engineers at the University of Wisconsin–Madison have developed a versatile new electrolyte that advances the development of an initially anode-free sodium-ion battery—a promising alternative to lithium-ion batteries for electric vehicles and grid energy storage. Led by Assistant Professor Fang Liu and PhD students Qianli Xing and Ziqi Yang, the team is also using this electrolyte as a model system to explore how molecular manipulation can improve compatibility between different battery components, potentially paving the way for more efficient and energy-dense battery technologies.

Typically, batteries are made up of two electrodes—an anode (negative side) and a cathode (positive side)—as well as a liquid electrolyte. In this case, the “initially anode-free” aspect of the battery means its physical anode forms internally upon the battery’s first charge—making it simpler, less expensive and more energy-dense.

Containing solvents and dissolved salts, the electrolyte is the liquid medium that touches all parts of the battery’s cells and, in its charging or discharging process, helps ions travel between the electrodes.

In a battery, the anode and cathode are different materials—for example, graphite, hard carbon sodium or lithium for the anode and a transition metal oxide like lithium nickel manganese cobalt oxide or sodium nickel iron manganese oxide for the cathode.

One of the challenges in developing next-generation batteries is that there’s not a one-size-fits-all electrolyte that performs effectively with both electrode material types. Conversely, when an electrolyte contains multiple solvent molecules, controlling their interactions and behavior is challenging.

Tweaking the electrolyte is a balancing act involving multiple factors, including how solvent molecules in the electrolyte form a “shell” around ions that could accelerate or impede the ions’ movement between anode and cathode—which ultimately affects battery charging and discharging, along with overall battery performance. “Using this model system, we are basically trying to understand whether we can present different molecules to different electrode surfaces—for example, an anode-stable solvent to the anode, and then a cathode-stable solvent to the cathode,” says Liu. “In this way, the electrolyte mixture would ideally behave like an anode-stable solvent at the anode, and like a cathode-stable solvent at the cathode.”

To create its new electrolyte, the team mixed two ether-based solvents, 2-methyltetrahydrofuran, or 2-MeTHF, which is more stable at the anode, and tetrahydrofuran, or THF, which is more stable at the cathode. Importantly, they found a way to rationalize electrolyte design: Solvents that dominate the first shell around positively charged ions that travel between electrodes are key to anode stability, while “free” or more weakly bonded solvents are important to the stability of the cathode side. “Through this electrolyte engineering work, we were trying to demystify what determines the stability of the anode and cathode separately, and how to present suitable molecules to both electrodes,” says Liu. “Qianli found out that the key factor is the population of solvents in the first solvation shell versus outside, and their location determines their presentation during the battery formation process.”

Computational testing, conducted by collaborator Reid Van Lehn, an associate professor of chemical and biological engineering at UW-Madison, and his student Jung Min Lee, played a significant role in the research as well. They used all-atom molecular dynamics simulations to predict the composition of solvent molecules near sodium ions and determine whether those ions “preferred” one solvent over the other. “Our results indeed found—in good agreement with experiments from the Liu group—that we could identify a single strongly interacting solvent (2-MeTHF) and a weakly interacting solvent (THF),” says Van Lehn. “We further used these calculations to relate this behavior to the relative strength of interactions of each type of solvent, providing molecular-scale insight that can be extended to even more complex mixtures to continue optimizing electrolyte design.”

The research lays the groundwork for the next steps in developing not simply sodium-metal batteries, but also other new alternatives to lithium-ion batteries. “Through this research, we start to understand that the solvent and anion interactions become really important,” says Liu. “We’re trying to expand our solvent library to manipulate these kinds of interactions, to see whether this kind of working principle can be applied to broader solvent libraries and different battery chemistries.”

For more information Nature Communications
Image: PhD student Qianli Xing. Photos: Joel Hallberg

New AI technique unravels quantum atomic vibrations in materials

Caltech researchers have developed a machine learning method that significantly accelerates quantum calculations related to atomic vibrations, or phonons, which influence key material properties like heat transport and phase transitions. Led by Professor Marco Bernardi and graduate student Yao Luo, the team built on their earlier work using singular value decomposition (SVD) to simplify complex mathematical models of electron-phonon interactions. Their new AI-based approach could eventually be applied to all quantum interactions, offering a powerful tool for understanding how particles behave in materials from first principles.

Now, inspired by recent advances in machine learning, Bernardi and Luo have developed an AI-based technique that sifts through the high-order tensors that encode phonon interactions in a material and extracts only the crucial bits needed to complete the calculations that explain thermal transport.

Using current state-of-the-art techniques, a supercomputer takes hours or days to calculate the interactions between three or four phonons in a material. The new method enables computers to complete the same thermal transport and phonon dynamics calculations 1,000 to 10,000 times faster, all while maintaining accuracy.

“The calculations for four-phonon interactions are a nightmare,” Bernardi says. “For complex materials, this task would involve weekslong calculations. Now we can do them in 10 seconds.”

Bernardi explains more about the method:

“We use a machine learning technique called CANDECOMP/PARAFAC tensor decomposition, but we had to adapt it to satisfy the symmetry of this specific physical problem. We first set up a neural network and then run it on GPUs and ask: ‘What are the best functions to approximate the actual tensor that describes these phonon interactions?’ Once we fix the number of product terms we want to keep, the machine learning process returns the best functions to approximate the full tensor. We typically only need a few of these products, saving orders of magnitude in computational complexity compared to using the full tensor. This method allows us to learn the compressed form of phonon interactions, and we can still use these highly compressed tensors to compute all the observables of interest with the same accuracy.”

Bernardi adds that the new method is well suited for high-throughput screening of thermal physics and heat transport in large material databases, a major effort in the materials community. As for future work, he says, “My vision right now is to compress all different types of quantum interactions and high-order processes in materials with similar techniques. The key will be to bypass the formation of large tensors altogether and to learn the interactions directly in compressed form.”

For more information: Physical Review Letters
Image: Inspired by recent advances in machine learning, Caltech scientists have developed an AI-based technique that sifts through the high-order tensors that encode phonon interactions in a material and extracts only the crucial bits needed to complete the calculations that explain thermal transport.

An accelerated paradigm for developing mission-critical materials

Scientists and engineers at Johns Hopkins Applied Physics Laboratory (APL) are pioneering a new approach to materials science that leverages artificial intelligence and robotics to dramatically speed up the design, testing, and optimization of metal components critical to national defense. This initiative, called TETRA (Transforming Evaluation and Testing via Robotics and Acceleration), reimagines the traditional materials science framework — known as the tetrahedron — by integrating advanced automation and accelerated testing methods. The goal is to overcome current limitations in the defense industrial base, which struggles to meet demand for both legacy and advanced metallic components due to slow alloy qualification processes.

Funded by the Department of Defense’s Industrial Base Analysis and Sustainment Program, TETRA aims to revolutionize how materials are evaluated, enabling rapid deployment of high-performance alloys. According to Sal Nimer, assistant program manager for APL’s Science of Extreme and Multifunctional Materials program, this effort could significantly enhance the speed and efficiency of producing and qualifying materials, helping the DoD maintain existing systems while unlocking new capabilities.

“When developing materials for defense needs, it’s not just about the composition of the alloy or system — it’s also about how you shape, treat and refine it,” said Morgan Trexler, who leads the research program area in APL’s Research and Exploratory Development Mission Area. “TETRA has potential to be game-changing because it allows us to simultaneously consider every variable that impacts performance, which until now, has been painstaking and time-consuming, sometimes taking months to achieve what TETRA can accomplish in just a matter of days.”
In materials science, processing, structure and properties are dynamically interrelated, with changes in one necessarily affecting the others. However, conventional processes lock scientists into procedures that force them to assess each factor serially, explained Paul Lambert, TETRA co-lead. Scientists typically produce a large ingot of material with a uniform chemical composition, cut it into pieces, place those in a furnace, machine each into a test specimen and then subject each specimen to analysis to test for properties of interest. This sequence is then iteratively repeated for each change made to the material.

“It takes a really long time, it’s really expensive and it’s inefficient,” Lambert said. “With the TETRA lab, we’re working to simultaneously explore all of the different composition and processing variants that influence properties and performance — or at least we aim to do this significantly more rapidly.”

Their approach leverages a method known as combinatorial synthesis to study a variety of chemical compositions. TETRA expands on the standard implementations, which are too limited in size and scale for the rigors of fielded equipment, Lambert explained.

“Materials perform quite differently when scaled up in size, so we are developing methods that focus on development and size scales of interest,” he said. “And traditional combinatorial synthesis often doesn’t account for critical effects of heat treatment and the hot work from forging and other production processes. Our approach will enable understanding and consideration for all of these effects as we develop new alloys and scalable processing approaches.”
TETRA is leveraging an additive manufacturing technique called blown-powder directed energy deposition, or DED. The process involves a laser melting metal powder as it’s fed into the build area, where it quickly solidifies. This allows for the creation, layer by layer, of dense metal structures, and chemical compositions can be varied in each sample. A single build plate can contain hundreds of alloys, printed into custom-designed 3D specimens, ready to be autonomously tested.

In addition to fabrication via additive manufacturing, the lab will feature a state-of-the-art melting furnace for ultrafast synthesis of custom castings from raw material, custom heat treatment furnaces and hot forging equipment for shaping material and modifying its microstructure, and robotic mechanical property measurement. This combination of capabilities will make TETRA an all-in-one materials research and development facility — the first of its kind.

These same tools for discovering new materials will also enable researchers to troubleshoot the manufacturing of legacy parts, Lambert said, helping to identify why a “surprisingly high” number of parts are rejected for poor properties, even when the root cause of these poor properties is not always clear. “One envisioned future use for the TETRA lab is to help diagnose those kinds of problems with existing parts, in addition to creating new ones,” he said.

Eventually, the TETRA team envisions bringing in existing APL capabilities that employ artificial intelligence to discover novel materials for extreme environments.

“TETRA’s cutting-edge methods should integrate seamlessly with our ongoing work in AI-accelerated materials discovery,” Nimer said. “We envision creating an AI ‘co-engineer’ that works alongside human researchers, learning from materials development data to automatically recommend the next tests, or even creating a self-running lab that autonomously designs materials and tests them. We’re not there yet, but we hope we’re building the foundation to enable those instantiations in the future.”

Image: A rendering of the TETRA lab demonstrates how the effort will develop novel capabilities and streamline processes to increase the speed of production for designing, testing and optimizing metal components. Credit: Johns Hopkins APL

Scientists report heavy electrons could open a path to a new type of quantum computer

Scientists in Japan have uncovered unusual quantum behavior in “heavy” electrons within the crystalline compound CeRhSn, which could one day support advances in quantum computing. These electrons appear to carry hundreds of times their normal mass—not due to their intrinsic properties, but because of strong interactions with other particles in the material that slow them down. Unlike typical metals, the electrons in CeRhSn enter a “non-Fermi liquid” state, moving collectively and entangled rather than individually. Remarkably, this state follows a universal energy dissipation rule known as Planckian scaling, linking the behavior to fundamental constants of nature.

n ordinary conductors like copper, electrons scatter in a way that can be calculated with standard physics. But at the edge of magnetism, superconductivity, or other collective phases, those rules break down. According to the researchers, CeRhSn sits right at this edge, making it a prime example of what physicists call “quantum criticality.”

The significance, according to the team, is that quantum critical materials may offer new routes for building quantum technologies. While most current quantum computers use superconducting circuits or trapped ions, heavy-electron compounds could provide an alternative platform where information is stored in the collective motion of electrons.

Dr. Shin-ichi Kimura of The University of Osaka, who led the research, said, “Our findings demonstrate that heavy fermions in this quantum critical state are indeed entangled, and this entanglement is controlled by the Planckian time. This direct observation is a significant step towards understanding the complex interplay between quantum entanglement and heavy fermion behavior.”

To probe CeRhSn, the team grew single crystals of the material in a controlled furnace and then polished them for study. They shined polarized light along different crystal directions and recorded how the electrons responded across a wide range of energies.

The experiments showed a distinct directional difference. In the plane where the cerium atoms form a kagome-like pattern—a triangular lattice with inherent frustration—the electrons followed Planckian scaling below about 80 Kelvin, or -193°C. Along the vertical axis, however, the electrons did not follow the same rule. The researchers interpret this anisotropy, or direction dependence, as evidence that the geometry of the lattice strongly shapes how the electrons behave.

While the findings demonstrate that heavy electrons can follow universal scaling laws, they do not yet provide a recipe for building a quantum computer. According to the study, the scaling behavior appeared only along one direction in the crystal, underscoring the material’s complexity.

The researchers also note that different experimental probes sometimes yield conflicting results. For example, while optical conductivity measurements suggested Planckian behavior, other measurements such as heat capacity report different values. Reconciling these differences will require further experiments.

Quantum computing today is built on platforms that manipulate single quantum states and properly managing entanglement. Although there is work to do, the study points to a different possibility: harnessing the collective entanglement of many strongly interacting electrons. While speculative, the researchers argue that observing Planckian scaling in heavy-electron systems adds weight to this idea.

The researchers suggest that CeRhSn may represent a new class of quantum critical material, distinct from compounds where magnetism dominates. They propose studying other materials with similar lattice structures to see if the same directional scaling appears. Pressure, chemical substitution, or magnetic fields could also be used to test how far the Planckian regime extends.

If the phenomenon proves robust, scientists report they could eventually try to design materials where the collective state of heavy electrons can be stabilized and controlled. Such systems might support qubits that are less sensitive to noise than those in existing technologies.

For more information: npj Quantum Materials

A simple metal could solve the world’s plastic recycling problem

Northwestern University chemists have developed a new plastic upcycling process that could revolutionize recycling by significantly reducing or even eliminating the need to pre-sort mixed plastic waste. Using an inexpensive nickel-based catalyst, the method selectively breaks down polyolefin plastics—such as polyethylenes and polypropylenes, which make up nearly two-thirds of global plastic use—allowing industrial users to efficiently process large volumes of unsorted waste.

When the catalyst breaks down polyolefins, the low-value solid plastics transform into liquid oils and waxes, which can be upcycled into higher-value products, including lubricants, fuels and candles. Not only can it be used multiple times, but the new catalyst can also break down plastics contaminated with polyvinyl chloride (PVC), a toxic polymer that notoriously makes plastics “unrecyclable.”

“One of the biggest hurdles in plastic recycling has always been the necessity of meticulously sorting plastic waste by type,” said Northwestern’s Tobin Marks, the study’s senior author. “Our new catalyst could bypass this costly and labor-intensive step for common polyolefin plastics, making recycling more efficient, practical and economically viable than current strategies.”

“When people think of plastic, they likely are thinking about polyolefins,” said Northwestern’s Yosi Kratish, a co-corresponding author on the paper. “Basically, almost everything in your refrigerator is polyolefin based — squeeze bottles for condiments and salad dressings, milk jugs, plastic wrap, trash bags, disposable utensils, juice cartons and much more. These plastics have a very short lifetime, so they are mostly single-use. If we don’t have an efficient way to recycle them, then they end up in landfills and in the environment, where they linger for decades before degrading into harmful microplastics.”

A world-renowned catalysis expert, Marks is the Vladimir N. Ipatieff Professor of Catalytic Chemistry at Northwestern’s Weinberg College of Arts and Sciences and a professor of chemical and biological engineering at Northwestern’s McCormick School of Engineering. He is also a faculty affiliate at the Paula M. Trienens Institute for Sustainability and Energy. Kratish is a research assistant professor in Marks’ group, and an affiliated faculty member at the Trienens Institute. Qingheng Lai, a research associate in Marks’ group, is the study’s first author. Marks, Kratish and Lai co-led the study with Jeffrey Miller, a professor of chemical engineering at Purdue University; Michael Wasielewski, Clare Hamilton Hall Professor of Chemistry at Weinberg; and Takeshi Kobayashi a research scientist at Ames National Laboratory.

From yogurt cups and snack wrappers to shampoo bottles and medical masks, most people interact with polyolefin plastics multiple times throughout the day. Because of its versatility, polyolefins are the most used plastic in the world. By some estimates, industry produces more than 220 million tons of polyolefin products globally each year. Yet, according to a 2023 report in the journal Nature, recycling rates for polyolefin plastics are alarmingly low, ranging from less than 1% to 10% worldwide.

The main reason for this disappointing recycling rate is polyolefin’s sturdy, stubborn composition. It contains small molecules linked together with carbon-carbon bonds, which are famously difficult to break.

“When we design catalysts, we target weak spots,” Kratish said. “But polyolefins don’t have any weak links. Every bond is incredibly strong and chemically unreactive.”

Currently, only a few, less-than-ideal processes exist that can recycle polyolefin. It can be shredded into flakes, which are then melted and downcycled to form low-quality plastic pellets. But because different types of plastics have different properties and melting points, the process requires workers to scrupulously separate various types of plastics. Even small amounts of other plastics, food residue or non-plastic materials can compromise an entire batch. And those compromised batches go straight into the landfill.

Another option involves heating plastics to incredibly high temperatures, reaching 400 to 700 degrees Celsius. Although this process degrades polyolefin plastics into a useful mixture of gases and liquids, it’s extremely energy intensive.

“Everything can be burned, of course,” Kratish said. “If you apply enough energy, you can convert anything to carbon dioxide and water. But we wanted to find an elegant way to add the minimum amount of energy to derive the maximum value product.”

To uncover that elegant solution, Marks, Kratish and their team looked to hydrogenolysis, a process that uses hydrogen gas and a catalyst to break down polyolefin plastics into smaller, useful hydrocarbons. While hydrogenolysis approaches already exist, they typically require extremely high temperatures and expensive catalysts made from noble metals like platinum and palladium.

“The polyolefin production scale is huge, but the global noble metal reserves are very limited,” Lai said. “We cannot use the entire metal supply for chemistry. And, even if we did, there still would not be enough to address the plastic problem. That’s why we’re interested in Earth-abundant metals.”

For its polyolefin recycling catalyst, the Northwestern team pinpointed cationic nickel, which is synthesized from an abundant, inexpensive and commercially available nickel compound. While other nickel nanoparticle-based catalysts have multiple reaction sites, the team designed a single-site molecular catalyst.

The single-site design enables the catalyst to act like a highly specialized scalpel — preferentially cutting carbon-carbon bonds — rather than a less controlled blunt instrument that indiscriminately breaks down the plastic’s entire structure. As a result, the catalyst allows for the selective breakdown of branched polyolefins (such as isotactic polypropylene) when they are mixed with unbranched polyolefins — effectively separating them chemically.

“Compared to other nickel-based catalysts, our process uses a single-site catalyst that operates at a temperature 100 degrees lower and at half the hydrogen gas pressure,” Kratish said. “We also use 10 times less catalyst loading, and our activity is 10 times greater. So, we are winning across all categories.”

With its single, precisely defined and isolated active site, the nickel-based catalyst possesses unprecedented activity and stability. The catalyst is so thermally and chemically stable, in fact, that it maintains control even when exposed to contaminants like PVC. Used in pipes, flooring and medical devices, PVC is visually similar to other types of plastics but significantly less stable upon heating. Upon decomposition, PVC releases hydrogen chloride gas, a highly corrosive byproduct that typically deactivates catalysts and disrupts the recycling process.

Amazingly, not only did Northwestern’s catalyst withstand PVC contamination, PVC actually accelerated its activity. Even when the total weight of the waste mixture is made up of 25% PVC, the scientists found their catalyst still worked with improved performance. This unexpected result suggests the team’s method might overcome one of the biggest hurdles in mixed plastic recycling — breaking down waste currently deemed “unrecyclable” due to PVC contamination. The catalyst also can be regenerated over multiple cycles through a simple treatment with inexpensive alkylaluminium.

“Adding PVC to a recycling mixture has always been forbidden,” Kratish said. “But apparently, it makes our process even better. That is crazy. It’s definitely not something anybody expected.”

For more information: Nature Chemistry

Novel kiri-origami structures enable high-performance stretchable electronics

Stretchable electronics are increasingly used in devices like smartphones, smartwatches, curved displays, and wearable sensors, but they face a trade-off between flexibility and electrical performance, as stretchable materials like elastomers typically underperform compared to rigid ones like metals or semiconductors. To address this, researchers have turned to origami and kirigami—Japanese techniques of folding and cutting paper—to enable stretchability in non-stretchable electronic materials. Origami creates bendable structures with hinges suitable for mounting rigid components, while kirigami uses slits to allow full structural deformation, making it ideal for large-area designs but less compatible with rigid parts.

In a groundbreaking study, Professor Eiji Iwase and Mr. Nagi Nakamura from the Department of Applied Mechanics and Aerospace Engineering at Waseda University, Japan, developed an innovative hybrid technique using kiri-origami structures.

“In this study, we have proposed a kiri-origami structure that incorporates both folding and cutting lines, combining the strengths of origami and kirigami while canceling out their weaknesses,” explains Iwase. “This structure enables large-number-of-unit, large area electronic devices, allowing rigid electronic components to be folded by stretching.”

The proposed kiri-origami design features a mutual orthogonal cutting line pattern. In this pattern, triangular joint panels consisting of two folding lines act as hinges and connect two square panels formed by the cutting lines. When stretched from a flat state, the square panels rise and rotate. This opens slits between the panels, ultimately resulting in a Z-shape around the hinges. This structure allows simultaneous mounting of rigid components and stretching to a target shape, while also supporting large-area and large-number-of-unit structures.

In ideal kiri-origami structures, called rigid-origami structures, the panels do not deform, and the hinges rotate frictionlessly. However, for a real stretchable electronic substrate, panel deformation and elastic repulsive forces cannot be overlooked, giving rise to an “elastic origami model.” To investigate these effects, the researchers tested the deformation of a rectangular elastic origami model using a simple stretching method, where the sample is clamped and stretched uniaxially. They observed that the elastic model deformed differently from the rigid model. They found that this difference occurs due to two factors: first, the clamping edges in the rigid model are free edges, while they are fixed in the elastic model. Second, the entire structure distorts while stretching due to non-uniform tension.

To mitigate these effects, the researchers developed a new folding method that introduces buffer structures. The buffer structures are trapezoidal extensions that connect all the edges of the kiri-origami structure to the clamps. The width of the shorter edge of the buffer structures is equal to the initial width of the kiri-origami structure, while the larger edge is set to the target stretched width of the rigid model. When a tensile force is applied, they extend and behave like springs. As a result, the entire structure stretches in two directions, matching the deformation of the rigid model while maintaining uniform tension.

The researchers demonstrated this technique by fabricating a stretchable display with more than 500 hinges and 145 LEDs. All hinges could fold up simultaneously, and the device’s performance was maintained before and after folding.

“Our approach makes it possible to develop stretchable electronic devices that can accommodate complex shapes and do not compromise on performance, including next-generation high-performance wearable sensors, curved displays, and flexible sensors and actuators for human assistance robots,” remarks Iwase.

This kiri-origami technique thus offers a scalable, structurally engineered solution for integrating high-performance electronic materials into flexible, stretchable devices—paving the way for innovative applications in electronics, healthcare, and robotics.

For more information: npj Flexible Electronics

Image: Kiri-origami structures combine the benefits of both origami and kirigami, incorporating their advantages while canceling their disadvantages, enabling the development of high-performance, stretchable, large-number-of-unit electronic devices.

MoonRanger’s instruments to gather data during 2029 lunar mission

NASA has tapped a lunar rover built at Carnegie Mellon University, Pittsburgh, to advance our understanding of water on the moon as it autonomously explores near the lunar south pole. MoonRanger will be among the payloads aboard a 2029 mission to the moon. The rover will carry a neutron spectrometer to study the lunar soil for traces of hydrogen, a good indicator of the presence of water, and demonstrate new levels of autonomous navigation on the moon.

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Accelerating materials design with high-throughput experiments and data science

Advances in machine learning and computational modeling are transforming materials design by allowing researchers to predict and discover new materials more efficiently than traditional methods. At the forefront of this innovation is Professor Toshiaki Taniike of the Japan Advanced Institute of Science and Technology (JAIST), who leads the Laboratory on Materials Informatics. His team integrates high-throughput experiments, data science, and simulations to accelerate the development of materials like catalysts, polymers, nanocomposites, and nanomaterials such as MOFs and graphene. Driven by a lifelong passion for science, Taniike is focused on solving real-world problems through smarter, data-driven approaches to materials discovery.

“I studied chemical engineering to learn the basics of process design. But I realized I needed a deeper understanding of how chemical reactions actually work. That led me to quantum physics and simulations. However, simulations cannot fully capture the complexity of real reactions, so I returned to experiments, focusing on catalysis, because catalysts drive about 80% of chemical processes,” says Prof. Taniike, sharing how he voyaged through this field. Over time, he learned that discovery often comes through trial and error, so now, he combines high-throughput experiments with data science and machine learning to make this process faster and smarter.

Traditionally, discovering new materials or chemical reactions was mainly pursued as a trial-and-error experiment. Researchers would use what they already knew to take a calculated guess about which features or descriptors matter most when designing a new material. For example, they might think that surface area or crystal structure will affect catalyst performance and then test that idea. This worked well for improving things we already understand, but its utility is limited for discovering truly new reactions or materials because you cannot guess what you do not know.

What Prof. Taniike’s lab does differently is to combine high-throughput experimentation with machine learning techniques like automatic feature engineering. Instead of relying on human intuition to choose descriptors, they let the machine automatically generate and test thousands or even millions of possible descriptors to find the ones that really matter. They then run experiments in parallel to test these ideas, which makes the process 10 to 1000 times faster than doing it manually. Moving beyond the traditional trial-and-error approach, this lets them discover reactions or materials that were impossible to find before.

One such example is the oxidative coupling of methane (OCM), which is sometimes called a “dream reaction.” It aims to convert methane — the most abundant hydrocarbon feedstock — directly into ethylene, which is extremely valuable for producing plastics and chemicals. This is very challenging because methane is such a stable molecule. Usually, when you try to activate it, it just burns completely to CO2. The idea behind OCM is to carefully control this process so that instead of complete combustion, you stop the reaction at the ethylene stage. This could help reduce CO2 emissions from the chemical sector. Published in ACS Catalysis in 2020, Prof. Taniike’s study presents a high-throughput system for the OCM, generating a large, consistent dataset that enables automated performance evaluation, insightful data visualization, and accurate C₂ yield prediction through nonlinear machine learning.

Another study published in Communications Chemistry presents a combination of automatic feature engineering with the above high-throughput system, to streamline the discovery of high-performing catalysts for the OCM through automated descriptor design.

The team is also moving toward discovering entirely new kinds of reactions that could someday transform fields like energy, carbon recycling, or sustainable chemical manufacturing.

For more information: Japan Advanced Institute of Science and Technology

Image: Professor TANIIKE Toshiaki from JAIST.

Scientists give robots a sense of touch with fabric that mimics human skin

Robots often struggle with tasks requiring a delicate sense of touch, such as grasping objects without dropping or crushing them, despite advancements in sensors and cameras. A team at the University at Buffalo (UB) is developing a new electronic textile (E-textile) that mimics the way human nerves detect pressure and slippage during gripping. This cost-effective technology could significantly improve robotic tactile sensitivity, enabling machines to handle objects more precisely and safely.

“The applications are very exciting …,” says Jun Liu, assistant professor in the Department of Mechanical and Aerospace Engineering, School of Engineering and Applied Sciences. “The technology could be used in manufacturing tasks like assembling products and packaging them — basically any situation where humans and robots collaborate. It could also help improve robotic surgery tools and prosthetic limbs.”

Liu, also a core faculty member of UB’s RENEW Institute, is the study’s corresponding author. Additional authors include Ehsan Esfahani, associate professor of mechanical and aerospace engineering, several UB students and a former UB PhD student from Liu’s group who is now a postdoctoral scholar at the University of Chicago.

“Our sensor functions like human skin — it’s flexible, highly sensitive and uniquely capable of detecting not just pressure, but also subtle slip and movement of objects,” says Vashin Gautham, a PhD candidate in the Liu research group and first author of the study. “It’s like giving machines a real sense of touch and grip, and this breakthrough could transform how robots, prosthetics and human-machine interaction systems interact with the world around them.”

Researchers integrated the sensing system onto a pair of 3D-printed robotic fingers that are mounted to a compliant robotic gripper developed by Esfahani’s group.

“The integration of this sensor allows the robotic gripper to detect slippage and dynamically adjust its compliance and grip force, enabling in-hand manipulation tasks that were previously difficult to achieve,” says Esfahani.

For example, when researchers tried to pull a copper weight from the fingers, the gripper sensed this and immediately tightened its grip.

“This sensor is the missing component that brings robotic hands one step closer to functioning like a human hand,” Esfahani adds. The slight movement of the object causes friction between the two materials, which in turn generates direct-current (DC) electricity — a phenomenon known as the tribovoltaic effect.

Researchers measured the sensing system’s response time and found it comparable to human capabilities. For example, it took the system from .76 milliseconds to 38 milliseconds to respond, depending on the experiment. Human touch receptors typically react between 1 and 50 milliseconds.

“The system is incredibly fast, and well within the biological benchmarks set forth by human performance,” says Liu. “We found that the stronger or faster the slip, the stronger the response is from the sensor. This is fortuitous because it makes it easier to build control algorithms to enable the robot to act with precision.”

The research team is planning additional testing of the sensing system, including integrating a form of artificial intelligence known as reinforcement learning that could further improve the robot’s dexterity.

For more information: Nature Communications

Image: From left: Ehsan Esfahani, Vashin Gautham and Jun Liu pose with the compliant robotic gripper outfitted with an electronic textile sensor. Photo: Meredith Forrest Kulwicki

A smarter approach to designing metamaterials

Lightweight cellular materials are essential to the performance of many industrial products, but defects during fabrication can compromise their effectiveness. To address this, a UC Berkeley-led team has developed GraphMetaMat, an AI-powered framework that uses deep learning to efficiently design 3D truss metamaterials with exceptional mechanical and acoustic properties, while reducing their vulnerability to manufacturing flaws and improving their overall usability.

“Until now, most of the work done in AI and materials design has been in the theoretical and computational domain, where they give you the design that performs well under ideal conditions,” said Xiaoyu (Rayne) Zheng, associate professor of materials science and engineering and the study’s principal investigator. “GraphMetaMat shows that AI can give you a realistic design tailored for a specific manufacturing method, like 3D printing, and optimized to withstand various manufacturing related defects. It sets the stage for the automatic design of manufacturable, defect-tolerant materials with on-demand functionalities.”

While advances in data-driven design and additive manufacturing have significantly accelerated the development of truss metamaterials, Zheng explained that existing inverse design approaches have inherent limitations. They can generate metamaterials with target linear properties, such as elasticity, but struggle to capture more complex nonlinear behaviors, such as energy absorption, needed for items like car bumpers and protective athletic gear.

“Design methods like topology optimization or an intuition-guided iterative approach are good at predicting simple responses,” said Zheng. “But for many real-world problems, these approaches cannot efficiently design materials with the required functionality, manufacturability and tolerance to defects introduced during manufacturing.”

Recently, researchers considered using graph neural networks for metamaterials design, since this has proved to be a powerful tool in drug discovery. But there was little to no training data available for designing metamaterials.

Zheng and his fellow researchers solved this problem by integrating multiple deep learning techniques — reinforcement learning, imitation learning, a surrogate model, and Monte Carlo tree search — into GraphMetaMat.

“Users can create metamaterial designs, represented as graphs, entirely from scratch based on custom inputs — such as a desired stress–strain curve or specific vibration attenuation gaps where mechanical waves are blocked at certain frequencies,” said Marco Maurizi, postdoctoral researcher in the Department of Materials Science and Engineering and lead author of the study. “Our AI system then iteratively adds graph nodes and edges to define the material’s geometry and topology.”

Most importantly, according to Zheng, GraphMetaMat can also integrate engineering constraints into the graphs — including manufacturing and defect constraints.

“GraphMetaMat has the unique ability to account for fabrication-induced imperfections,” he said. “This innovation is a game-changer because it ensures that the generated metamaterials will not fail if they develop a small defect during manufacturing.”

In their proof of concept, the researchers used GraphMetaMat to design lightweight truss metamaterials optimized for energy absorption and vibration mitigation at various frequencies. For each use case, the generated metamaterial consistently outperformed traditional materials, including polymeric foams and phononic crystals.

“Based on our findings, GraphMetaMat has the potential to redefine the design paradigm,” said Zheng. “This opens the door to exciting new possibilities in creating realistic, high-performance metamaterials.”

This work was conducted in collaboration with UCLA researchers led by Wei Wang and Yizhou Sun, and Penn State University researchers led by Yun Jing. In addition, co-lead authors of this study include Derek Xu of UCLA and Yu-Tong Wang of Penn State University.

For more information: Nature Machine Intelligence

Image: GraphMetaMat, an inverse design framework, enables users to create metamaterial designs, represented as graphs, entirely from scratch based on custom inputs. Its AI system then iteratively adds graph nodes and edges to define the material’s geometry and topology and integrates manufacturing and defect constraints. (Illustration courtesy of the researchers)

Self-driving lab to automate the discovery of novel alloys

Pure metals like aluminum and titanium often lack the ideal combination of properties needed for advanced applications, prompting researchers to develop custom alloys by blending various elements. To accelerate this traditionally slow and complex process, scientists at Lawrence Livermore National Laboratory, in collaboration with Cornell University, are creating the APEX platform—an automated system for 3D printing, processing, and analyzing alloy samples—to dramatically reduce alloy development time from years to months.

Funded by LLNL’s Laboratory Directed Research and Development program, this platform will combine industry-matured robotics and automation technologies with cutting-edge machine learning capabilities, turning a traditionally tedious and repetitive research process into a fully automated cycle.

“Our end goal is to make APEX the first self-driving laboratory for alloy discovery at LLNL, capable of working around the clock to collect experimental data and autonomously design, build and test novel alloys,” said Mason Sage, APEX principal investigator and LLNL robotics engineer.

Researching new alloys is complex for several reasons, but primarily because the design space expands exponentially with each added processing or compositional variable.

“Imagine you’re baking cookies and want to create the perfect recipe, so you decide to experiment with three ingredients: sugar, butter and flour,” Sage said. “If you test each ingredient with 10 different amounts, you’d need to bake 1,000 cookies to try every possible combination (10 sugar variations × 10 butter variations × 10 flour variations). Now, imagine you have 20 or 30 ingredients to test, suddenly, the number of cookies you’d need to bake becomes astronomically high — somewhere around 100 quintillion combinations, or 100 with eighteen zeros following it.

“This is the challenge before us. In theory, there hasn’t been enough time since the universe began to test every possible combination — even if we conducted an experiment every second.”

To navigate this enormous and complex parameter space, APEX leverages robotics and machine learning to accelerate sample fabrication, preparation and inspection by intelligently exploring different combinations.

“Our goal is to use machine learning to analyze the information we have collected from all our processes, learn from past experiments and design new experiments,” Sage said.

After selecting an alloy to explore, the APEX platform builds the metal sample layer-by-layer using an additive manufacturing method called directed-energy deposition, a type of 3D printing that uses a high-power laser to melt and fuse metal powders onto a substrate.

Then, to prepare a printed sample to be microstructurally and mechanically characterized, its surface must undergo grinding and polishing. Traditionally, these steps can take two to three days to complete by hand, but APEX is expected to simultaneously process multiple samples at a time, producing dozens of samples a day.

Once a sample is prepped, APEX can evaluate its properties through a series of characterization tests, including examining its surface under a microscope, indenting it with a diamond to measure hardness and crushing it to assess its response to compression.

Finding ways to automate these labor-intensive steps has been a central challenge for the APEX team, one that is critical to the project’s success.

“Trying to not only determine what is really important to our research process but also how to turn that into something you can control a robot to do has been an interesting undertaking,” said LLNL research scientist Michael Juhasz.

For example, when a researcher grinds a sample, they do so by feel, tuning into the vibrational patterns of the machine and sample to ensure they are applying the right amount of pressure. This left the team wondering: How do you relay this highly developed skill to a robotic system? One creative solution the team came up with was to strap a vibration sensor to the machine to capture these “feelings” numerically.

“The sensor produces data in real time, allowing us to put numbers and parameters to the different vibrations, which can guide APEX when it’s grinding a sample,” said LLNL research scientist Alex Baker. “This project is really trying to understand where the automation complexity meets the material science complexity and then translating between the two.”

From sample fabrication to grinding and polishing to characterization, APEX works to collect data at every phase, generating a full history for each sample that passes through the platform. In the future, APEX aims to integrate this data with the Materials Acceleration Platform (MAP), combining physics-based models with machine learning to help design and improve the next round of APEX-produced samples.

The MAP is a framework for integrating computer models that uses algorithms to design optimal materials under different constraints (limitations related to how material properties interact and impact one another) in an effort to find an ideal composition that embodies all desired properties. This is particularly important because optimizing materials often involves trade-offs, where improving one property can sometimes lead to compromises in another. MAP helps strike a balance to achieve the best possible combination of properties.

While one model might excel at predicting a material’s hardness, another might be better suited for predicting its ductility.

“MAP connects all of these separate, siloed models and weaves them together to help us predict new materials that we haven’t tested yet,” said LLNL computational scientist Brandon Bocklund. “The vision of the project is to really close the loop of materials development and acceleration — letting the system learn and update the models autonomously.”

Currently in the early stages of its development, APEX is being trained to work with stainless steel alloys as the research team proves its feasibility.

“We chose stainless steel because it is very a well characterized alloy,” Juhasz said. “This makes it the ideal control, allowing us to ground what APEX is doing and perfect the system before diving deeper into our materials development research.”

The plan is to make APEX as extensible as possible by its anticipated completion in 2027, allowing researchers to adapt the platform to accommodate a variety of unique scientific challenges and incorporate new characterization or diagnostic techniques as needed.

This one-of-a-kind project exemplifies LLNL’s multidisciplinary approach to pushing the boundaries of scientific discovery, demonstrating that when experts from different fields come together, no problem is too “unsolvable.”

 “It’s been great seeing the synergies between deep scientific knowledge of the materials experts meshing with the technical expertise of the engineers,” Sage said.

Baker echoed this sentiment, “There have been many instances where we each could see the subject matter expertise the other side of the project was bringing.”

For more information: Lawrence Livermore National Laboratory

Tailored hard/soft magnetic heterostructure anchored on 2D carbon nanosheet for efficient microwave absorption and anti-corrosion property

Electromagnetic wave pollution has become a growing concern due to the widespread use of electronic devices, prompting increased research into electromagnetic absorbing materials. Recently, focus has shifted to developing heterogeneous materials combining soft and hard magnetic components, though challenges like weak interfacial coupling and impedance mismatch remain, highlighting the need for precise control of their nanostructures to fully leverage magnetic interface engineering.

A team of material scientists led by Dong Wang from Shandong University of Technology, China recently studied the state of tailored hard/soft magnetic heterostructure for efficient microwave absorption and anti-corrosion property to advance research in the field. The synthesized Fe3C/ZnFe2O4/C (FZC) shows wide EAB of 4.56 GHz and RLmin value of -65.6 dB. By layer-to-layer stacking of two-dimensional (2D) FZC and reduced graphene oxide (rGO), the obtained flexible rGO/FZC-1 film can effectively shield 5G signals. Importantly, both the 2D morphology and abundant heterostructures restrain the diffusion of saline ions inside the FZC coatings and enhance the “maze effect”, finally improving the corrosion resistance in marine environment.

“In this research, we present a soft/hard magnetic heterostructures of ZnFe2O4/Fe3C, which are anchored on 2D carbon nanosheets, are successfully tailored by in-situ blowing gel process. Soft magnetic ZnFe2O4 nanoparticles and hard magnetic Fe3C nanoparticles are crosslinked with each other, forming a large number of heterogeneous interfaces. Such soft/hard magnetic heterogeneous interfaces generate sufficient magnetic exchange coupling interaction, and enhance polarization loss. 

“Moreover, the clever introduction of 2D carbon sheets balances the impedance matching and endow the composites with dielectric loss. The synthesized Fe3C/ZnFe2O4/C-1 (FZC-1) shows wide EAB of 4.56 GHz and RLmin value of -65.6 dB. RCS simulation results further confirm that the FZC-1 has great application prospects in stealth coatings. Density functional theory (DFT) calculations demonstrate the exchange coupling effect, which results from the dynamic charges reconstruction of soft and hard magnetic heterogeneous interface. Moreover, by layer-to-layer stacking of 2D FZC and reduced graphene oxide (rGO), the obtained flexible rGO/FZC-1 composite film can effectively shield 5G signals. Importantly, both the 2D morphology and abundant heterostructures restrain the diffusion of saline ions inside the FZC coatings and enhance the “maze effect”, finally greatly enhancing the corrosion resistance in marine environment,” said Dong wang, senior author, a professor in the School of Materials science and Engineering at Shandong University of Technology and vice president of the Institute of Engineering Ceramics.

For more information: Nano Research

Image: Tailored soft/hard magnetic heterostructures of ZnFe2O4/Fe3C, which are anchored on two-dimensional (2D) carbon nanosheets, are synthesized by high temperature gel blowing process. Benefitting from the integration of soft/hard magnetic heterostructure, carbon component, and 2D morphology, the obtained 2D Fe3C/ZnFe2O4/C shows excellent electromagnetic wave (EMW) absorbing performance and high corrosion resistance.

An experimental quantum chip may yield more robust qubits

Microsoft’s newly unveiled prototype chip, Majorana 1, has emerged as a strong contender in the race to build practical quantum computers. Unlike traditional computers, quantum machines promise to tackle complex problems—such as simulating vast molecular interactions for drug discovery—at unprecedented speeds. What sets Microsoft’s approach apart is its use of a rare state of matter, making it one of the most unconventional and potentially more stable methods among current quantum technologies, which often rely on manipulating atoms with lasers or magnetic fields.

The chip is the most advanced attempt yet to harness the properties of a strange type of particle that was first predicted to exist in the 1930s by Italian theoretical physicist Ettore Majorana, for whom Microsoft’s chip is named. Majorana’s particle, called a Majorana fermion, remained entirely theoretical for more than seventy years. That is, until researchers (many of them supported by the U.S. National Science Foundation) realized that thin-layered arrangements of certain solid materials might tease out the behavior of the elusive particles and perhaps even put them to use. One of those researchers was theoretical physicist Chetan Nayak.  

In 2000, Nayak was at UCLA, where he received an NSF Faculty Early Career Development (NSF CAREER) grant to explore the physical attributes and theoretical potential of systems that might be made with newly discovered materials now known as topological materials (more on those later). Nayak joined Microsoft in 2005, where he now leads the company’s effort to develop a functional quantum computer by physically implementing some of his NSF-funded theories.  

“NSF was really important in supporting my career in its early stages, without a doubt,” says Nayak. “NSF was there at the start.” 

Microsoft is one of many companies racing to produce a useful quantum computer. A powerful quantum computer could potentially help scientists find new superconducting materials, new medicines and other valuable substances hidden within the practically infinite variety of nature.  

“Genuinely transformative new technologies require entirely new concepts,” says Daryl Hess, program director in the NSF Division of Materials Research. Hess oversaw Nayak’s NSF CAREER grant from 2000 to 2006. “We can’t go down that road without bold new ideas thoughtfully woven together through theory and experiment.”

“We’re basically doing stuff at the one-qubit level,” says Nayak of the currently modest ability of the Majorana 1 to potentially control qubits, the basic unit of information in a quantum computer.  

“That’s pretty basic, but it’s a whole new type of qubit. It’s a whole new way of controlling qubits and doing computations.” 

Quantum mechanics was a young field when Ettore Majorana proposed the existence of a new particle in a 1937 paper. It details his mathematical exploration of equations created by British physicist Paul Dirac, which describe the properties of subatomic particles. From Majorana’s mathematical manipulations, out popped a strange theoretical particle that could be coursing through your body and you’d never know it: the now-eponymous Majorana fermion. 

A fermion is a category of elementary particle (the smallest, most basic particles of matter) that includes electrons, protons and neutrons. Unlike those more familiar fermions, Majorana’s equations predicted a particle that would not interact much with anything. That evasiveness would make Majorana fermions largely invisible to particle colliders and other instruments scientists use to detect and understand elementary particles.  

And so, Majorana’s particle remained entirely theoretical for over 70 years. It wasn’t until the 2010s that physicists came up with new experiments that might reveal the particles by using custom-made nanowires made of a newly found type of matter.

When cooled to near absolute zero and with certain voltages applied, researchers hypothesized that Majorana fermions might be detected at the tips of the nanowires, not as individual particles, but in the collective behavior of many electrons flowing on the surface of the wire.

Scientists call such collective particle behavior a “quasiparticle,” and it’s akin to the wave created by thousands of fans at a stadium standing and sitting in coordination. The key to evoking that stadium-like “wave” of quasiparticle behavior was in the stuff the nanowires were made of: a topological material.  

To the naked eye, a topological material might look indistinguishable from, say, a ceramic tile. But topological materials are very different from your bathroom tiles, and in a peculiar way.

The outer surface of a topological material has properties unlike its interior. And yet, the entire material (both surface and interior) is made of a single, uniform substance. Topological materials get their name from topology, a branch of mathematics that describes properties of shapes that do not change even when deformed in certain ways.  

Scientists have discovered a number of topological materials with seemingly contradictory, yet measurably different and persistent, surface properties — that distinction makes topological materials unique compared to all other known states of matter.

One type of topological material is called a topological insulator because electrical current flows only on its surface, while its interior remains electrically insulating. And, that surface-level flow of electrons is unimpeded by cutting, scratching or otherwise changing the material. Other topological materials allow the unrestricted flow of light or even sound along their surface. 

Microsoft’s Majorana 1 chip — a complex device that uses nanowires made of topological materials and operates at near absolute zero — is based on the idea that Majorana fermions can be used to create qubits. While the state, or value, of a regular computer bit can be either zero or one, a qubit can be in multiple states simultaneously. That uniquely quantum phenomenon is called superposition, and it’s key to how quantum computers can theoretically solve problems far too complex for even the fastest supercomputers in use today.  

While there are several different ways to create qubits, their superposition is generally fragile and easily spoiled by slight environmental changes, like temperature or light. Creating qubits using materials with topologically persistent properties is one way to potentially reduce that fragility.  

Topological computers have greater stability, explains Nayak. “Certain features are really robust and stable.”

Whether using topological materials or other techniques, it will take considerably more research and development before quantum computers are capable of overtaking classical computers.

“Quantum computers would help us understand, predict and discover yet more new states of matter and related phenomena,” says Hess. “Those currently undiscovered states may provide the foundations of as-yet unimagined quantum-based technologies.” 

From fundamental scientific discovery to application, realizing any transformative technology requires time, investment and sustained effort.

Some experts are skeptical about whether the Majorana 1 actually demonstrates functional qubits made from the elusive fermions first predicted by Ettore Majorana nearly 90 years ago. Nonetheless, Nayak and team at Microsoft are continuing to push ahead to validate and scale up the technology with the goal of eventually creating a practically useful quantum computer that is fast, controllable and stable.

“The process of discovery is long and complex, and it took years of hard work from many just to get to this point,” says Alex Klironomos, senior advisor in the NSF Division of Materials Research. “If you want to enable major technological advances, you have to take calculated scientific risks.”  

For more information: Nature

Image: A close-up of some of the intricate features on Microsoft’s Majorana 1 chip.(Credit: John Brecher for Microsoft)