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Machine Learning for Materials Research: Autonomous Phase Mapping

  • October 24, 2018
  • RSVP by Oct 19 to
  • Kim Engineering Building, College Park , MD , US


Machine Learning for Materials Research: Autonomous Phase Mapping

Dr. Gilad Kusne, National Institute of Standards and Technology

The last few decades have seen significant advancements in materials research tools, allowing researchers to rapidly synthesis and characterize large numbers of samples - a major step toward high-throughput materials discovery. Machine learning has been tasked to aid in converting the collected materials property data into actionable knowledge, and more recently it has been used to assist in experiment design. In this talk we present the next step in machine learning for materials research - autonomous materials research systems. We first demonstrate autonomous measurement systems for phase mapping, followed by a discussion of ongoing work in building fully autonomous systems. For the autonomous measurement systems, machine learning controls X-ray diffraction measurement equipment both in the lab and at the beamline to identify phase maps from composition spreads with a minimum number of measurements. The algorithm also capitalizes on prior knowledge in the form of physics theory and external databases, both theory-based and experiment-based, to more rapidly hone in on the optimal results. Materials of interest include Fe-Ga-Pd, TiO2-SnO2-ZnO, and Mn-Ni-Ge.

About the speaker:
Dr. Gilad Kusne is a Staff Scientist with the National Institute of Standards and Technology (NIST). He integrates machine learning with physics theory, simulation, experiment, and databases to develop autonomous experiments for materials discovery and to provide live data analysis tools for experimentalists. His research also includes leading the development of optimization algorithms for genetics-based cellular sensors and exploring methods for optimizing smart city design. Dr. Kusne received his B.S., M.S., and Ph.D. degrees from Carnegie Mellon University, and is an Adjunct Professor with the University of Maryland.

DATE: Wednesday, October 24, 2018

6:00 pm Chapter volunteers meeting in the Atrium
6:30 pm Social/networking
7:15 pm Dinner
8:00 pm Presentation
8:30 pm Q&A


The University of Maryland Kim Engineering Building - Kay Boardrooms (1107 and 1111)
8228 Paint Branch Drive, College Park MD 20742, at the intersection of Stadium Drive


Parking: Closest lots are H and GG2 (small), and 11B (large), all free after 4 pm. See map.
Metro: Information for connecting via the free UMD Shuttle from the College Park metro can be found here. Just hop on (no ID required) and get off at Stadium Drive. You can check the schedule on the NextBus app.


Members & Guests $20
Full time students $10

RSVP by October 19 to

No-shows cost the chapter money, so please plan to attend if you register!

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