Position Title
Toyota Research Institute
Dr. Joseph Montoya
Abstract
Accelerating Discovery in Computational Materials Science using CAMD
Artificial intelligence and machine learning are enabling automation of decision-making in various scientific domains, but still face a number of fundamental obstacles in materials science. We provide an overview of our platform, Computational Autonomy for Materials Discovery (CAMD), which helps materials scientists simulate and design their discovery processes using machine learning tools. CAMD is engineered to maximize the likelihood that sequential iterations of an experimental or simulation-based workflow will produce materials data with target properties. To date, CAMD’s primary application is in the prediction of new, phase-stable crystal structures from structural prototypes in various chemical spaces. In addition, we have begun designing multi-fidelity sequential learning agents using data streams from experiments and theory. We review these capabilities with a view towards the future of AI-assisted tools for materials discovery.
Biography
Joseph Montoya is a Senior Research Scientist in the Energy and Materials team at the Toyota Research Institute. His background is in DFT-based electrocatalyst simulation and the development of high-throughput DFT workflows. At TRI, he is developing machine-learning-based tools to enable scientists and engineers to discover new materials more quickly in applications like corrosion-resistant alloys, fuel cell catalysts, and batteries.