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

Publication ,  Journal Article
Kusne, AG; McDannald, A; DeCost, B; Oses, C; Toher, C; Curtarolo, S; Mehta, A; Takeuchi, I
Published in: Frontiers in Physics
February 16, 2022

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.

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Published In

Frontiers in Physics

DOI

EISSN

2296-424X

Publication Date

February 16, 2022

Volume

10

Related Subject Headings

  • 51 Physical sciences
  • 49 Mathematical sciences
 

Citation

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Kusne, A. G., McDannald, A., DeCost, B., Oses, C., Toher, C., Curtarolo, S., … Takeuchi, I. (2022). Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping. Frontiers in Physics, 10. https://doi.org/10.3389/fphy.2022.815863
Kusne, A. G., A. McDannald, B. DeCost, C. Oses, C. Toher, S. Curtarolo, A. Mehta, and I. Takeuchi. “Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping.” Frontiers in Physics 10 (February 16, 2022). https://doi.org/10.3389/fphy.2022.815863.
Kusne AG, McDannald A, DeCost B, Oses C, Toher C, Curtarolo S, et al. Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping. Frontiers in Physics. 2022 Feb 16;10.
Kusne, A. G., et al. “Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping.” Frontiers in Physics, vol. 10, Feb. 2022. Scopus, doi:10.3389/fphy.2022.815863.
Kusne AG, McDannald A, DeCost B, Oses C, Toher C, Curtarolo S, Mehta A, Takeuchi I. Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping. Frontiers in Physics. 2022 Feb 16;10.

Published In

Frontiers in Physics

DOI

EISSN

2296-424X

Publication Date

February 16, 2022

Volume

10

Related Subject Headings

  • 51 Physical sciences
  • 49 Mathematical sciences