月別: 2024年2月

[Published online Journal of Computer Chemistry, Japan Vol.22, 41-49, by J-STAGE]
<Title:> スピン反転凍結軌道解析を用いた円錐交差構造の支配因子に関する理論的研究
<Author(s):> 五十幡　康弘, 吉川　武司, 中井　浩巳, 小川　賢太郎, 坂田　健
<Corresponding author E-Mill:> ikabata.yasuhiro.lz(at)tut.jp
<Abstract:> S₀/S₁極小エネルギー円錐交差(MECI)の支配因子を評価するために，スピン反転時間依存密度汎関数理論に対する凍結軌道解析(FZOA)の波動関数と励起エネルギーを導出した．スピン反転法に特有のスピン汚染を避けるため，定式化においてスピン完全法を適用した．数値計算の結果，「HOMO-LUMO交換積分がほぼ0となる」，「HOMO-LUMOギャップの上限値はHOMO，LUMOが関係するCoulomb積分によって定まる」というS₀/S₁ MECIの支配因子を発見した．本論文では，FZOAについて概説するとともに，スピン反転法におけるFZOAの定式化について述べる．導出したスピン反転FZOAの式をエチレンとウラシルに適用した結果に基づいて，励起エネルギー成分に基づく支配因子の発見，S₀/S₁ MECIの電子構造，これらの制限付き開殻法における結合係数への依存性について解説する．
<Keywords:> Conical intersection, Time-dependent density functional theory, Spin-flip method, Frozen orbital analysis, Restricted open-shell method
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0021/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.22, 34-36, by J-STAGE]
<Title:> 分子軌道エネルギーを用いた機械学習によるオクタノール/水分配係数log Pの予測
<Author(s):> 寺前　裕之
<Corresponding author E-Mill:> teramae(at)gmail.com
<Abstract:> Octanol/water partition coefficient, log P, is an important parameter in classical QSAR. The new method using machine learning which we propose uses only the molecular orbital energy as an explanatory variable and does not include log P. Therefore, since the log P value can be predicted using the molecular orbital energy, we speculated that log P may not be necessary as a result if sufficient number of molecular orbital energies would be given as parameters.
<Keywords:> octanol/water partition coefficient, equilibrium geometries, eigenvalues of molecular orbital, machine learning, molecular orbital energies
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0022/_article/-char/ja/

[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/