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  • Review Article
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Accelerating the prediction of stable materials with machine learning

Nature Computational Science volume 3, pages 934–945 (2023)Cite this article

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Abstract

Despite the rise in computing power, the large space of possible combinations of elements and crystal structure types makes large-scale high-throughput surveys of stable materials prohibitively expensive, especially for complex materials and materials subject to environmental conditions such as finite temperature. When physics-based computational methods and labor-intensive experiments are not feasible, machine learning (ML) methods can be a rapid and powerful alternative. Owing to a wealth of experimental and first-principles data as well as improved ML frameworks designed for materials modeling, ML is shown to be effective in predicting stability parameters and accelerating the discovery of new stable materials. In this Review, we summarize the most recent advancements in applying ML methodologies in predicting materials stability, focusing particularly on predictions of zero- and finite-temperature stability. We also highlight the need for more ML development in predictions of other thermodynamic knobs, such as pressure and surface/interfacial energy, which practically impact materials stability.

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Fig. 1: Illustration of how the convex hull can vary with temperature.
Fig. 2: Examples of recent ML frameworks for material stability predictions.
Fig. 3: Workflow to discover new stable compounds using elemental substitution.
Fig. 4: Examples of recent ML models for predicting vibrational free energy of ordered compounds at T > 0.
Fig. 5: Examples of recent ML models for predicting disordered compounds at T > 0.

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Acknowledgements

We acknowledge funding from the US Department of Commerce and National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD) under award no. 70NANB19H005. We also acknowledge the Air Force Office of Scientific Research for support under award no. FA9550-18-1-0136. Y.X. gratefully acknowledges the start-up fund provided by Portland State University.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA

    Sean D. Griesemer, Yi Xia & Chris Wolverton

  2. Department of Mechanical and Materials Engineering, Portland State University, Portland, OR, USA

    Yi Xia

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  1. Sean D. Griesemer
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S.D.G. conceived of and wrote the paper. Y.X. wrote much of the content regarding finite-temperature ML methods. C.W. supervised the work and provided critical feedback.

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Correspondence to Chris Wolverton.

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Nature Computational Science thanks Wan-Jian Yin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team.

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Griesemer, S.D., Xia, Y. & Wolverton, C. Accelerating the prediction of stable materials with machine learning. Nat Comput Sci 3, 934–945 (2023). https://doi.org/10.1038/s43588-023-00536-w

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