投稿者: 博佐藤

[Published online Journal of Computer Chemistry, Japan Vol.23, 9-12, by J-STAGE]
<Title:> キャリアの視点から見た有機太陽電池の発電機構の考察
<Author(s):> 成島　和男, 藤笠　舜大, 岡田　直之
<Corresponding author E-Mill:> narushim(at)ube-k.ac.jp
<Abstract:> Organic thin-film solar cells, which have attracted attention as a renewable energy source in recent years, provide important benefits of being light weight, flexible, inexpensive, and produced from a virtually inexhaustible resource. This report explains the power generation principle of organic thin-film solar cells by particularly addressing the movement of conduction electrons and holes, based on knowledge introduced in a paper and letters presented at this conference, augmented with findings from the calculation results reported here. The following important findings must be considered. (1) Regarding the bimolecular system of phthalocyanine and fullerene C₆₀, even in the configuration calculated for this study, some parts of the phthalocyanine molecule had low probability of electron cloud existence. (2) For this configuration, a change was found in the electronic state in the excited state compared to the ground state. The power generation mechanism of organic thin-film solar cells was considered from the carrier perspective.
<Keywords:> Organic thin-solar cells, Quantum chemistry calculations, Electron cloud, Power generation mechanism, Carrier perspective.
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0004/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 1-3, by J-STAGE]
<Title:> p-フェニルエチニル化合物群の π 電子共役の強さを適正に評価する量子化学計算の探索
<Author(s):> 鈴木　椋子, 千葉　康平, 奥山　克彦
<Corresponding author E-Mill:> okuyama.katsuhiko(at)nihon-u.ac.jp
<Abstract:> We searched for quantum chemical calculations that would properly evaluate strength in the π-electron conjugation of a group of p-phenylethynyl compounds determined by molecular spectroscopy with error ranges. The target molecules are tolane, 1,4-diphenylbutadiyne and 1,4-bis(phenylethynyl)benzene. Quantum chemical calculations were performed with B3LYP, CAM-B3LYP, ωB97X-D, M06-2X, and MP2 for theory, and basis sets were explored among the combinations 6-311++G(d) cc-pVDZ, and aug-cc-pVDZ. Because B3LYP calculation is considered to be unskilled at evaluating dispersion force, a dispersion force correction term is added to B3LYP calculation. The least of square of the difference in the strength between observed and calculated was given by calculations with M06-2X/aug-cc-pVDZ. It has, however, often been reported that M06-2X undergoes inappropriate structural optimization. In calculations for 1,4-diphenylbutadiyne, M06-2X was not giving systematic uniform results. Overall, ωB97X-D was a functional function corrected for long-range and dispersion force correction, and it was considered to give good results together with CAM-B3LYP corrected for dispersion force.
<Keywords:> Keyword p-phenylethynyl compounds, strength in π-electron conjugation, dispersion force correction, ωB97X-D/aug-cc-pVDZ, molecular spectroscopy
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2023-0044/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 4-8, by J-STAGE]
<Title:> FMOプログラムABINIT-MPの整備状況2023
<Author(s):> 望月　祐志, 中野　達也, 坂倉　耕太, 奥脇　弘次, 土居　英男, 加藤　季広, 滝沢　寛之, 成瀬　彰, 大島　聡史, 星野　哲也, 片桐　孝洋
<Corresponding author E-Mill:> fullmoon(at)rikkyo.ac.jp
<Abstract:> In August 2023, we released the latest version of our ABINIT-MP program, Open Version 2 Revision 8. In this version, the most commonly used FMO-MP2 calculations are even faster than in the previous Revision 4. It is now also possible to calculate excitation and ionization energies for regions of interest. Improved interaction analysis is also available. In addition, we have started GPU-oriented modifications. In this preliminary report, we present the current status of ABINIT-MP.
<Keywords:> キーワードFragment molecular orbital, FMO, ABINIT-MP, Supercomputer, A64FX, SX-Aurora TSUBASA, GPU
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/1/23_2024-0001/_article/-char/ja/

[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/

[Published online Journal of Computer Chemistry, Japan Vol.22, 28-30, by J-STAGE]
<Title:> 量子多体系ダイナミクスシミュレータの構築
<Author(s):> 戸畑　海研, 石田　邦夫
<Corresponding author E-Mill:> ishd_kn(at)cc.utsunomiya-u.ac.jp
<Abstract:> We propose a quantum dynamics simulator for light-irradiated matter. As the coherent control of the quantum states of matter is an intriguing problem in realizing quantum technology, the quantum properties of light-matter complexes have attracted much attention. Theoretical calculations corresponding to individual experimental configurations are required to understand these properties, and simulation methods that flexibly adapt to various experimental configurations are required. In this paper, we propose a quantum dynamics simulation method that encompasses program modules corresponding to light sources, optical components, material systems, and the evaluation of physical properties. We demonstrate the entanglement generation dynamics between two spin-chains via irradiated photons and show that a flexible and expandable quantum dynamics simulator can be constructed.
<Keywords:> Quantum many-body system simulator, Quantum dynamics calculation, Modularized simulator
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0024/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.22, 18-20, by J-STAGE]
<Title:> Theoretical Analysis of the Aggregation-Inhibition Effect of Arginine on Polyglutamine Protein by the Generalized-Ensemble Method
<Author(s):> Shoichi TANIMOTO, Hisashi OKUMURA
<Corresponding author E-Mill:> sktanimoto(at)ims.ac.jp
<Abstract:> The aggregation of polyglutamine (polyQ) proteins, which have the abnormal expansion of glutamine repeats, is a critical pathological hallmark of polyQ diseases. Experimental studies have shown an amino acid arginine uniquely inhibits the polyQ-protein aggregation. We performed replica-permutation molecular dynamics simulations to clarify the inhibitory effects of arginine on the polyQ-protein aggregation. We found arginine makes more contact with the polyQ protein than lysine, and this tendency of arginine likely inhibits the polyQ-protein aggregation.
<Keywords:> Molecular dynamics simulation, Generalized-ensemble algorithm, Polyglutamine, Protein-aggregation inhibition, Amyloid
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0020/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.22, 21-23, by J-STAGE]
<Title:> 化学系特許中の表及びテキストからの材料知識データ抽出
<Author(s):> 我妻　正太郎, 竹内　理
<Corresponding author E-Mill:> shotaro.agatsuma.hd(at)hitachi.com
<Abstract:> Material Informatics (MI) needs large amount of data about materials. However, the cost of data extraction is very high. Therefore, chemical researchers are interested in technology to automatically extract the data from published documents such as patents. Previous technologies can extract the data from text in patents, but not tables. Therefore, we develop the data for MI extraction method from texts and tables in patents. In our evaluation, our method can reduce the time of data extraction by one-half. In the results, it can be expected that the new method can sufficiently reduce the cost of data extraction.
<Keywords:> Materials informatics, Machine learning, Natural Language Processing, Data Extraction
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0023/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.22, 12-14, by J-STAGE]
<Title:> 量子化学計算と各種スペクトル情報を用いた化合物の自動同定手法の開発
<Author(s):> 熊谷　拓海, 中嶋　裕也, 清野　淳司
<Corresponding author E-Mill:> j-seino(at)aoni.waseda.jp
<Abstract:> Recent practical application of automated experiments using robotics, high-throughput experiments, and artificial intelligence technology has been progressing rapidly. In automated experiments, molecular identification is an important process for obtaining structural information on synthesized compounds and understanding their reactivity and chemical properties. In this study, we developed a system for automated molecular identification. The system uses spectral information and quantum chemical calculations, which provide no fluctuating data and have a potential to explore a wide range of chemical space. Numerical validation results suggested that the system is capable of efficient and accurate automated molecular identification in organic compounds with low molecular weight.
<Keywords:> Molecular identification; Quantum chemical calculation; Spectral data; Molecular generator; Organic compound
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0029/_article/-char/ja/