Validation of Extrapolation in Symbolic Regression andIts Application to Perovskite Catalysts

DOI Web Site 3 References
  • ISODA Takuya
    Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555
  • TAKAHASHI Shiori
    Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555
  • NAKANO Masahiko
    Waseda Research Institute for Science and Engineering, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555 Mitsubishi Chemical Group, 1-1-1, Marunouchi, Chiyoda-ku, Tokyo, 100-8251
  • NAKAJIMA Yuya
    Waseda Research Institute for Science and Engineering, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555
  • SEINO Junji
    Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555 Waseda Research Institute for Science and Engineering, 3-4-1, Okubo, Shinjuku-ku, Tokyo, 169-8555

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Other Title
  • シンボリック回帰における外挿性の検証とペロブスカイト触媒への応用

Abstract

<p>The recent advances in artificial intelligence (AI) have accelerated the development of data-driven modeling. Complex machine learning models often lack interpretability. Symbolic regression, particularly in the fields of mathematics and physics, has provided alternative models that are interpretable and have excellent extrapolation capabilities. In this study, we investigated the potential of symbolic regression in chemistry, specifically in the exploration of new materials through extrapolation. We conducted fundamental verification of extrapolation and applied research on the exploration of perovskite catalysts using the recursive-LASSO-based symbolic regression. Our results suggested that symbolic regression exhibits superior extrapolation performance and interpretability compared to conventional machine learning methods.</p>

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