月別: 2024年5月

[Published online Journal of Computer Chemistry, Japan Vol.23, 27-29, by J-STAGE]
<Title:> アルキル鎖長が異なる有機半導体の電子物性差異に関する分子動力学および量子化学計算
<Author(s):> 岡田　智悠, 鈴木　朝香, 井上　悟, 長谷川　達生, 松井　弘之
<Corresponding author E-Mill:> t221488d(at)st.yamagata-u.ac.jp
<Abstract:> The experimental mobilities of organic semiconductors, 2-C_n-BTNT (Figure 1), depend on the alkyl chain length n, while the mechanism is unclear. In this study, we elucidate the relationship between alkyl chain length and carrier mobility by classical force field (FF) and density functional theory (DFT) calculations. We analyzed the interactions between an alkyl chain and surrounding molecules and executed NVT molecular dynamics simulations at room temperature and ambient pressure by FF calculations. Transfer integrals were calculated using the DFT method with the coordinates after NVT simulations. As a result, alkyl chain length affects the lattice constant, thermal motion of the alkyl chain and transfer integrals and gives the difference of mobilities.
<Keywords:> キーワード Organic Semiconductors, Molecular Dynamics, Density Functional Theory, Transfer Integrals, Mobility
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0005/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 24-26, by J-STAGE]
<Title:> 開始剤ラジカルの極性パラメータの算出
<Author(s):> 川内　進
<Corresponding author E-Mill:> skawauchi(at)quemix.com
<Abstract:> This paper describes a method for calculating the intrinsic e parameter of initiator radicals by applying the intrinsic Q-e scheme to the addition reaction between initiator radicals and monomers. Furthermore, DFT calculations show that there is a good relationship between the intrinsic e-value of initiator radicals and their electronegativity and Hammett constant, indicating that the e-value is a suitable parameter to describe the polar effect of initiator radicals.
<Keywords:> キーワード Radical polymerization, Initiator radical, Q-e value, polarity parameter, DFT calculation
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0003/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, A9-A14, by J-STAGE]
<Title:> ポリマーラジカルのQ-e値の初めての算出
<Author(s):> 川内　進
<Corresponding author E-Mill:> skawauchi(at)quemix.com
<Abstract:> Using our recently derived intrinsic Q-e scheme, unknown reactivity ratios of monomer pairs can be expressed without arbitrariness using only the reactivity ratios of the target and reference monomers, resulting in a high prediction accuracy of reactivity ratios. Furthermore, the Q-e values of polymer radicals and monomers can be calculated separately for the first time. In this paper, we introduce the intrinsic Q-e scheme, followed by the calculation of Q-e values for monomers and radicals.
<Keywords:>
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2023-0026/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 19-23, by J-STAGE]
<Title:> Machine Learning in Catalysis: Analysis and Prediction of CO Adsorption on Multi-elemental Nanoparticle using Metal Coordination-based Regression Model
<Author(s):> Susan Menez ASPERA, Gerardo Valadez HUERTA, Yusuke NANBA, Kaoru HISAMA, Michihisa KOYAMA
<Corresponding author E-Mill:> aspera_susan(at)shinshu-u.ac.jp
<Abstract:> Information about molecular adsorption strength is important in every catalytic reaction. The ability to compare and determine relevant molecular active sites of interaction is necessary for fast screening of potential catalysts specially in a vast spectrum of probable candidates. In this study, we used the metal-coordination of the adsorption sites as a descriptor of the adsorption energy of CO on the PtRuIr ternary alloy nanoparticle. Using multiple regression model, we are able to predict the adsorption energy and specify some important descriptors that controls the strength of CO adsorption energy. This will enable a fast prediction of CO adsorption energy on PtRuIr nanoparticles with varying compositions and possible different morphologies using only the information of the structure of the catalyst. And open up the possibility of predicting adsorption interaction of other combinations of alloys with higher number of metallic compositions for fast screening of appropriate molecule-surface interaction.
<Keywords:> multi-elemental nanoparticle alloy, machine learning, generalized coordination number, CO adsorption, PtRuIr ternary alloy, multiple regression
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0006/_article/-char/ja/