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A perspective on Bayesian methods applied to materials discovery and design

  • Computational Approaches for Materials Discovery and Development Prospective
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Abstract

For more than two decades, there has been increasing interest in developing frameworks for the accelerated discovery and design of novel materials that could enable promising and transformative technologies. The Integrated Computational Materials Engineering (ICME) program called for integrating computational tools to establish linkages along process–structure–property–performance chains. The Materials Genome Initiative called for integrating experiments and computations within data science frameworks as a strategy to accelerate the materials development cycle. While these frameworks and paradigms have been quite influential, traditional ICME or data science-based approaches tend to have some limitations, mainly when querying the materials space is costly and very little information is available. Bayesian methods are more suitable in this context due to their efficiency gains. To this end, the materials discovery problem is framed as a Bayesian optimization (BO). Different examples in which BO has been applied to solve materials discovery problems are presented. The methods/examples discussed include BO under model uncertainty, multi-information source BO, multi-objective and multi-constraint BO, and batch BO. Bayesian Materials Discovery is a promising area of research that is likely to become more influential as more attention is put on autonomous materials discovery platforms. Therefore, a discussion is provided on the potential development of such methods to increase the ability of existing platforms in materials discovery. The ultimate goal is to pave the way to autonomous materials discovery.

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Reproduced from Talapatra et al.[38]

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Acknowledgments

The authors would like to acknowledge the groups of Profs. Ed Dougherty, Dimitris Lagoudas, Ibrahim Karaman, and Seyede Fetemeh Ghoreishi. The authors also acknowledge Drs. Sahin Boluki, Anjana Talapatra, and Jaylen James. The authors acknowledge the financial support from NSF through Grants No. NSF-CMMI-1663130, NSF-CISE-1835690, NSF-CDSE-2001333, and NSF-DMREF-2119103. BV acknowledges the support of NSF through Grant No. NSF-DGE-1545403. The authors also wish to acknowledge the support from the U.S. Department of Energy (DOE) ARPA-E ULTIMATE Program through Project DE-AR0001427. Calculations used to demonstrate many of the BO methods described in this work were carried out at the High-Performance Research Computing (HPRC) facility at Texas A&M University.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Texas A&M University, College Station, TX, 77843, USA

    Raymundo Arróyave, Brent Vela, Richard Couperthwaite, Abhilash Molkeri & Ankit Srivastava

  2. J. Mike Walker ‘66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA

    Raymundo Arróyave, Danial Khatamsaz & Douglas Allaire

  3. Wm Michael Barnes ‘64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA

    Raymundo Arróyave

  4. Ames Laboratory, U.S. Department of Energy, Iowa State University, Ames, IA, 50011, USA

    Prashant Singh & Duane D. Johnson

  5. Department of Materials Science & Engineering, Iowa State University, Ames, IA, 50011, USA

    Duane D. Johnson

  6. Department of Electrical and Computing Engineering, Texas A&M University, College Station, TX, 77843, USA

    Xiaoning Qian

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  1. Raymundo Arróyave
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Arróyave, R., Khatamsaz, D., Vela, B. et al. A perspective on Bayesian methods applied to materials discovery and design. MRS Communications 12, 1037–1049 (2022). https://doi.org/10.1557/s43579-022-00288-0

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