Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Journal Article (Journal Article)

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.

Full Text

Duke Authors

Cited Authors

  • Kusne, AG; McDannald, A; DeCost, B; Oses, C; Toher, C; Curtarolo, S; Mehta, A; Takeuchi, I

Published Date

  • February 16, 2022

Published In

Volume / Issue

  • 10 /

Electronic International Standard Serial Number (EISSN)

  • 2296-424X

Digital Object Identifier (DOI)

  • 10.3389/fphy.2022.815863

Citation Source

  • Scopus