Givens回転と誤差逆伝播法を組み合わせた自己無撞着場計算の高効率収束法の開発 [Published online J. Comput. Chem. Jpn., 24, 36-38, by J-STAGE]

[Published online Journal of Computer Chemistry, Japan Vol.24, 36-38, by J-STAGE]
<Title:> Givens回転と誤差逆伝播法を組み合わせた自己無撞着場計算の高効率収束法の開発
<Author(s):> 大島 玲生, 中井 浩巳
<Corresponding author E-Mill:> nakai(at)waseda.jp
<Abstract:> We have recently proposed a highly efficient convergence method for self-consistent (SCF) calculations combining Givens rotation and error back-propagation (EBP) algorithms, referred to as direct Givens rotation (DGR) method [J. Chem. Phys. 162, 014108 (2025)]. The Givens rotation corresponds to unitary transformations that guarantee the orthogonality of molecular orbitals. Complicated gradients constructed through sequential Givens rotations were computed using the EBP technique without deriving explicit forms. This article reviews the proposed DGR method and compares it with conventional methods such as the standard SCF procedure, the second-order SCF method, and the direct inversion in iterative subspace technique for H2O molecule. The DGR method exhibited a convergence speed comparable to that of the SOSCF method while achieving a lower and more reliable energy.
<Keywords:> キーワード self-consistent field, direct minimization, Givens rotation, error back-propagation, Hartree-Fock, Kohn-Sham density functional theory
<URL:> https://www.jstage.jst.go.jp/article/jccj/24/1/24_2025-0003/_article/-char/ja/

立体電子状態の定量評価による求核反応の面選択性の起源の解明 [Published online J. Comput. Chem. Jpn., 24, 18-24, by J-STAGE]

[Published online Journal of Computer Chemistry, Japan Vol.24, 18-24, by J-STAGE]
<Title:> 立体電子状態の定量評価による求核反応の面選択性の起源の解明
<Author(s):> 坂口 大門, 五東 弘昭
<Corresponding author E-Mill:> gotoh-hiroaki-yw(at)ynu.ac.jp
<Abstract:> 化学反応の反応性や選択性の起源の解明は学術的に興味深いだけでなく,反応を制御するうえでも重要である.我々は最近,環状ケトンの立体的な電子状態を定量評価して求核反応の面選択性を定量的に説明し解釈する新しい方法を提案した [1].本稿では日本コンピュータ化学会2024年秋季年会での口頭発表の内容 [2] に基づき,化学反応と立体電子状態に関するこれまでの研究と,我々が開発した新しい方法について紹介する.
<Keywords:> Keywords ketone, nucleophilic addition, π-facial selectivity, QSSR, data-driven chemistry
<URL:> https://www.jstage.jst.go.jp/article/jccj/24/1/24_2024-0043/_article/-char/ja/

PdおよびAgRh合金ナノクラスターへの水素吸収と拡散に関する相対論的量子化学計算分子動力学シミュレーション [Published online J. Comput. Chem. Jpn., 24, 27-29, by J-STAGE]

[Published online Journal of Computer Chemistry, Japan Vol.24, 27-29, by J-STAGE]
<Title:> PdおよびAgRh合金ナノクラスターへの水素吸収と拡散に関する相対論的量子化学計算分子動力学シミュレーション
<Author(s):> 木村 愛花, 安藤 耕司
<Corresponding author E-Mill:> ando_k(at)lab.twcu.ac.jp
<Abstract:> Molecular dynamics simulations of hydrogen absorption and diffusion in Pd and AgRh alloy nanoclusters were performed. Parameters for atomic interaction potentials were determined from relativistic quantum chemical calculations. Although Rh is known not to absorb hydrogen in bulk, the Rh-H interaction strength obtained from the calculations was about 80% of that of the Pd-H. Using the parameters obtained, molecular dynamics simulations were performed. Molecular dynamics simulations of AgRh clusters with different Ag:Rh ratio have shown no significant difference in the Rh-Rh distance distribution at the nearest neighbor except for Ag70Rh30. However, at the second nearest neighbor the distance increased as the Ag fraction increased.
<Keywords:> Keywords Molecular dynamics simulation, Relativistic Quantum Chemical Calculations, Hydrogen-absorbing metal nanoclusters
<URL:> https://www.jstage.jst.go.jp/article/jccj/24/1/24_2024-0040/_article/-char/ja/

Predicting CO Adsorption on Multi-elemental Alloy through Machine Learning Analysis of Ternary Components [Published online J. Comput. Chem. Jpn., 24, 30-35, by J-STAGE]

[Published online Journal of Computer Chemistry, Japan Vol.24, 30-35, by J-STAGE]
<Title:> Predicting CO Adsorption on Multi-elemental Alloy through Machine Learning Analysis of Ternary Components
<Author(s):> Susan Menez ASPERA, Gerardo ALADEZ HUERTA, Yusuke NANBA, Kaoru HISAMA, Michihisa KOYAMA
<Corresponding author E-Mill:> aspera_susan(at)shinshu-u.ac.jp
<Abstract:> Surface-molecule interaction has always been an integral part in the analysis of reactions and surface reactivity in heterogenous catalysis. With the advent of computational resource advancement, the search for the next generation catalysts explores the combination of several metal elements in the multi-elemental nanoparticles (NP). However, with the complexity of the catalysts’ surface comes the difficulty of understanding surface-molecule interaction and methods to overcome this should be considered. In our previous study, we used metal-coordination to predict CO adsorption on the ternary alloy combinations. This method describes the adsorption site by the network of metal elements interacting with the site that contributes to the change in its electronic properties. Since this network considers up to the neighbor of the first nearest neighbor of the adsorption site, in this study, we considered to use the dataset of regression coefficient of ternary alloy combinations to predict molecular adsorption on a multi-elemental NP. We tested the method on CO molecular adsorption on the quaternary RuRhIrPt NP and used the regression coefficient obtained from RuRhPt, RhIrPt, RuIrPt and RuRhIr ternary alloy. Results show that the predicted values of CO adsorption energy on the RuRhIrPt NP have comparable values of coefficient of determination (R2) and mean absolute error (MAE) with the prediction of CO adsorption on ternary alloy. Thus, this method could pave the way for predicting molecular adsorption on multi-elemental NP using only the dataset of regression coefficient from ternary alloy combinations and the atomic configurational structure of the multi-elemental NP.
<Keywords:> Multi-elemental nanoparticle alloy, Machine learning, Generalized coordination number, Adsorption prediction
<URL:> https://www.jstage.jst.go.jp/article/jccj/24/1/24_2025-0002/_article/-char/ja/