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First-principles database for fitting a machine-learning silicon interatomic force field

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Abstract

Data-driven machine learning has emerged to address the limitations of traditional methods when modeling interatomic interactions in materials, such as electronic density functional theory (DFT) and semi-empirical potentials. These machine-learning frameworks involve mathematical models coupled to quantum mechanical data. In the present article, we focus on the moment tensor potential (MTP) machine-learning framework. More specifically, we provide an account of the development of a preliminary MTP for silicon, including details pertaining to the construction of a DFT database.

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Acknowledgments

We thank Dr. Yaoting Zhang for insightful discussions. The authors wish to thank Compute Canada for generous allocation of computer resources, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Nuclear Waste Management Organization (NWMO) for financial support. There is no conflict of interest. Data will be made available upon request.

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Authors and Affiliations

  1. Ecole de Technologie Supérieure, Université du Québec, Montreal, QC, Canada

    K. Zongo & C. Ouellet-Plamondon

  2. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, ON, Canada

    L. K. Béland

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  1. K. Zongo
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Correspondence to C. Ouellet-Plamondon.

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Zongo, K., Béland, L.K. & Ouellet-Plamondon, C. First-principles database for fitting a machine-learning silicon interatomic force field. MRS Advances 7, 39–47 (2022). https://doi.org/10.1557/s43580-022-00228-z

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