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Interpretable machine learning for materials design

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Abstract

Fueled by the widespread adoption of machine learning and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in silico prediction of materials properties. When training models to predict material properties, researchers often face a difficult choice between a model’s interpretability and performance. We study this trade-off by leveraging four different state-of-the-art machine learning techniques: XGBoost, SISSO, Roost, and TPOT for the prediction of structural and electronic properties of perovskites and 2D materials. We then assess the future outlook of the continued integration of machine learning into materials discovery and identify the key problems that will continue to challenge researchers as the size of the literature’s datasets and complexity of models increases. Finally, we offer several possible solutions to these challenges with a focus on retaining interpretability and share our thoughts on magnifying the impact of machine learning on materials design.

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Data availability 

Copies of the datasets used in this work can be found at Exabyte’s GitHub (https://github.com/Exabyte-io/Scientific-Projects/tree/Updates_29_09_22/DigitalEcosystem/raw_data) in the form of serialized Python objects (pkl files).

Code availability

Jupyter (Python) notebooks are available on Exabyte’s GitHub (https://github.com/Exabyte-io/Scientific-Projects/tree/Updates_29_09_22), which contains code to reproduce our results and figures.

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Funding

This research was supported by the US Department of Energy (DoE) Small Business Innovation Research (SBIR) program (grant no. DE-SC0021514). Computational resources were provided by Exabyte Inc. T.P. and M.S. were funded by the NOMAD CoE (Novel Materials Discovery Center of Excellence, European Union’s Horizon 2020 research and innovation program, grant agreement N\(^\circ\) 951786), the project TEC1p (European Research Council, grant agreement N\(^\circ\) 740233), and the project FAIRmat (FAIR Data Infrastructure for Condensed-Matter Physics and the Chemical Physics of Solids, German Research Foundation, project N\(^\circ\) 460197019). T.P. would like to thank the Alexander von Humboldt Foundation for their support through the Alexander von Humboldt Postdoctoral Fellowship Program.

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Authors and Affiliations

  1. Exabyte Inc., San Francisco, CA, USA

    James Dean & Timur Bazhirov

  2. University of California Santa Barbara, Isla Vista, CA, USA

    Matthias Scheffler

  3. The NOMAD Laboratory at the Fritz Haber Institute, Berlin, Germany

    Matthias Scheffler & Thomas A. R. Purcell

  4. Intermolecular Inc., San Jose, CA, USA

    Sergey V. Barabash

  5. Polaron Analytics, Beavercreek, OH, USA

    Rahul Bhowmik

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Contributions

JD and TB were responsible for performing the calculations and preparing the manuscript. TP performed and advised on the SISSO calculations. MS, RB, SB, and TB guided and conceptualized the project. All authors contributed in revising and writing the manuscript.

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Correspondence to Timur Bazhirov.

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James Dean carried out this work while employed by Exabyte Inc. Timur Bazhirov is currently employed by Exabyte Inc.

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Dean, J., Scheffler, M., Purcell, T.A.R. et al. Interpretable machine learning for materials design. Journal of Materials Research 38, 4477–4496 (2023). https://doi.org/10.1557/s43578-023-01164-w

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