[Published online Journal of Computer Chemistry, Japan Vol.22, 37-40, by J-STAGE]
<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
<Author(s):> 磯田 拓哉, 高橋 栞, 中野 匡彦, 中嶋 裕也, 清野 淳司
<Corresponding author E-Mill:> j-seino(at)aoni.waseda.jp
<Abstract:> 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.
<Keywords:> Machine Learning, Materials Informatics, Symbolic Regression, Material Science, Perovskite Catalysts.
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0028/_article/-char/ja/
<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
<Author(s):> 磯田 拓哉, 高橋 栞, 中野 匡彦, 中嶋 裕也, 清野 淳司
<Corresponding author E-Mill:> j-seino(at)aoni.waseda.jp
<Abstract:> 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.
<Keywords:> Machine Learning, Materials Informatics, Symbolic Regression, Material Science, Perovskite Catalysts.
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0028/_article/-char/ja/
