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 […]
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 […]






