Journal of Computer Chemistry, Japan

[Published online Journal of Computer Chemistry, Japan Vol.23, 105-114, by J-STAGE]
<Title:> FMO法の相互作用情報を用いた相分離シミュレーションとの連携
<Author(s):> 奥脇　弘次, 土居　英男, 小沢　拓, 望月　祐志
<Corresponding author E-Mill:> okuwaki(at)rikkyo.ac.jp
<Abstract:> Recent efforts have focused on utilizing molecular interaction data from FMO calculations for phase separation simulations in materials design. Accurate prediction of phase separation, which is closely related to molecular affinity, has long been a challenge due to difficulties in calculating accurate interaction parameters. We have developed a framework for estimating effective interaction parameters between coarse-grained components using FMO calculations. In addition, a simulation scheme called FMO-DPD, which applies these parameters to dissipative particle dynamics (DPD) simulations, has demonstrated its effectiveness in various systems. These developments are discussed in this paper.
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<URL:> https://www.jstage.jst.go.jp/article/jccj/23/4/23_2024-0029/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 115-125, by J-STAGE]
<Title:> 分子動力学法とFMO法を用いた非晶質固体分散体の吸湿安定性解析
<Author(s):> 松本　穂香, 奥脇　弘次, 東　顕二郎, 古石　誉之, 福澤　薫, 米持　悦生
<Corresponding author E-Mill:> okuwaki.koji(at)jsol.co.jp
<Abstract:> While Amorphous solid dispersion is an effective method for improving the solubility of pharmaceutical formulations, it presents stability challenges, as moisture absorption can accelerate crystallization. In this study, we employed a model system using four drug molecules (Droperidol, Nifedipine, Indomethacin, Ketoprofen) and the carrier polymer Polyvinylpyrrolidone, where molecular distribution was simulated using MD methods, and interaction energies were calculated using the FMO method. We analyzed the changes in interaction energies due to moisture absorption, clarifying the differences in crystallization tendencies and stability of the drugs.
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<URL:> https://www.jstage.jst.go.jp/article/jccj/23/4/23_2024-0031/_article/-char/ja/

[Advanced Published online Journal of Computer Chemistry, Japan, by J-STAGE]
<Title:> 結晶学を活用した結晶モデル作成支援ソフトウェア「壺車」の2024年更新版
<Author(s):> 洋陽 日沼
<Corresponding author E-Mill:> y.hinuma(at)aist.go.jp
<Abstract:> The author’s code for VASP POSCAR handling is updated with new methods developed in 2024. Size “1/N”supercells can be obtained to find identical or smaller size unit cells that may belong to a Bravais lattice with a lower degree of freedom if deviations from rigid requirements for each Bravais lattice are allowed. Sortable number “message digests” of the configuration of different species on a (sub)lattice can be derived, which is useful for eliminating identical configurations in exhaustive combinatorial searches of species distributions. A molecule containing a given atom can be extracted from, for example, a molecular crystal supercell with many molecules.
<Keywords:> Keywords Crystal model generation, 1/N-supercell, Bravais lattice, message digest, molecule detection
<URL:> https://www.jstage.jst.go.jp/article/jccj/advpub/0/advpub_2024-0034/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan -International Edition Vol.10, -, by J-STAGE]
<Title:> A Study of Deep Learning for Quantitative Analysis of Vitamin A in Cattle Blood
<Author(s):> Mizuki SHIBASAKI, Tetsuhito SUZUKI, Moriyuki FUKUSHIMA, Shin-ichi NAGAOKA, Yuichi OGAWA, Naoshi KONDO
<Corresponding author E-Mill:> nagaoka.shinichi.3c(at)kyoto-u.ac.jp
<Abstract:> Deep learning in combination with fluorescence excitation-emission spectroscopy was studied to quantitatively analyze vitamin A (retinol) in cattle blood. The neural network model being obtained with the deep learning predicted the vitamin-A levels with a coefficient of determination (R²) of 0.93 with respect to the experimental values. The combination of the deep learning and fluorescence excitation-emission spectroscopy has a potential to predict the vitamin-A level in the cattle blood accurately, rapidly and inexpensively and to improve production of marbled beef with maintaining cattle health. It could also be applied to quantitative vitamin-A assays of various biological tissues, foods and so on as well as to those of blood samples besides cattle.
<Keywords:> Deep learning, Neural network, Fluorescence excitation-emission spectroscopy, Vitamin A, Retinol, Cattle blood, random forest
<URL:> https://www.jstage.jst.go.jp/article/jccjie/10/0/10_2024-0012/_html

[Published online Journal of Computer Chemistry, Japan -International Edition Vol.10, -, by J-STAGE]
<Title:> Development of a Simultaneous Quantification for Hydrogen and Ammonia Using Deep Neural Network and The Rotating Ring Disk Electrode Method
<Author(s):> Yusuke HIRUMA, Hyunho KANG, Hidenobu SHIROISHI
<Corresponding author E-Mill:> h-shiroishi(at)tokyo-ct.ac.jp
<Abstract:> Ammonia is considered as a viable candidate for a hydrogen carrier in a hydrogen-based society. Currently, real-time analysis of products in the development of highly active ammonia electrolytic synthesis catalysts is limited to the extremely expensive technique of electrochemical mass spectrometry. In this study, we aimed to develop a real-time simultaneous quantification method for hydrogen and ammonia concentrations using the rotating ring-disk electrode technique. We adopted deep neural network-based machine learning technology using the data from cyclic voltammogram (CV) measurements on a Pt ring electrode. The sum of squared residuals decreased to 1/0.645 at hydrogen partial pressure and 1/92.8 at ammonia concentration compared to the conventional combination of nonlinear least-squares method and solving simultaneous equations. Moreover, classifying concentrations through image recognition based on the obtained CV images resulted in successful concentration determination with an accuracy of 78.9%.
<Keywords:> Nitrogen reduction, Rotating ring disk, Real-time, Quantitative analysis, Deep neural network
<URL:> https://www.jstage.jst.go.jp/article/jccjie/10/0/10_2024-0011/_html

[Published online Journal of Computer Chemistry, Japan Vol.23, 80-83, by J-STAGE]
<Title:> 分子軌道エネルギーを説明変数とした機械学習による薬効予測
<Author(s):> 寺前　裕之, 三浦　優太, 色摩　光一, 玄　美燕, 高山　淳, 岡﨑　真理, 坂本　武史
<Corresponding author E-Mill:> teramae(at)gmail.com
<Abstract:> We constructed a mathematical model to predict the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging capacity (IC₅₀) for recently synthesized ferulic acid derivatives by machine learning with molecular orbital energy as an explanatory variable and IC₅₀ as an objective variable. We compared 96 regression models including xgbLinear and neuralnet included in R/caret package. We were able to construct IC₅₀ prediction models for these new ferulic acids by using xgbLinear, M5, ppr, and neuralnet as regression methods.
<Keywords:> DPPH free radical scavenging capacity, pharmalogical activity, ferulic acid, IC50, machine learning
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/3/23_2024-0020/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 78-79, by J-STAGE]
<Title:> BZ反応を利用した物理レザバー計算による時系列予測および音声認識
<Author(s):> 都城　宏治, 香取　勇一, 田中　吉太郎, 髙木　清二, 櫻沢　繁
<Corresponding author E-Mill:> sakura(at)fun.ac.jp
<Abstract:> It is known that it is possible to develop a chemically intelligent robot that can perceive spatial extent using the BZ reaction. If the BZ reaction can be used for learning, there is a possibility of developing a chemically intelligent robot that can make complex judgments about situations like living organisms. In the physical reservoir calculation, it is possible to construct a learning system using a material system, so in this study, we build a physical reservoir calculation using the BZ reaction and confirmed that learning is possible. The physical reservoir computation was capable of performing tasks such as time series prediction and speech recognition. BZ reaction, nonlinear chemical reaction, reservoir computing, time series prediction, speech recognition
<Keywords:> キーワードBZ反応, 非線形化学反応, レザバー計算, 時系列予測, 音声認識
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/3/23_2024-0024/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 92-104, by J-STAGE]
<Title:> ABINIT-MPプログラムの現状と今後
<Author(s):> 望月　祐志, 中野　達也, 坂倉　耕太, 土居　英男, 奥脇　弘次, 加藤　季広, 滝沢　寛之, 大島　聡史, 星野　哲也, 片桐　孝洋
<Corresponding author E-Mill:> fullmoon(at)rikkyo.ac.jp
<Abstract:> The fragment molecular orbital (FMO) program ABINIT-MP has a quarter-century history, and related research and development of the Open Version 2 series is currently underway. This paper first summarizes the current status of the latest Revision 8 (released on August 2023). It then describes future improvements and enhancements, including GPU support. The connection with coarse-grained simulation (dissipative particle dynamics) and the possibility of cooperation with quantum computation are also touched upon.
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<URL:> https://www.jstage.jst.go.jp/article/jccj/23/4/23_2024-0022/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 85-91, by J-STAGE]
<Title:> フラグメント分子軌道法で生成したデータを用いた機械学習モデルの開発
<Author(s):> 加藤　幸一郎, 松本　大夢, 喜多　亮介
<Corresponding author E-Mill:> kato.koichiro.957(at)m.kyushu-u.ac.jp
<Abstract:> フラグメント分子軌道(FMO)法はタンパク質全体を量子化学計算可能な稀有な手法である．そして，FMO法によって得られるデータもまた，現状ではタンパク質系の量子化学計算データとして唯一無二のものとなっている．汎用ソフトウェアでは生成が困難なタンパク質の量子化学計算データとそれを用いた様々な機械学習モデルの開発は，近年活性化が著しいAI創薬に大きなインパクトを与えることが期待される．本稿では，筆者らのグループで進めているFMOデータを用いた機械学習モデル(原子電荷予測モデル，相互作用予測モデル，機械学習力場)の開発状況を概説する．
<Keywords:> Keyword Fragment Molecular Orbital Method, Machine Learning Force Field, Inter Fragment Interaction Energy, Atomic Charge, Neural Network
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/4/23_2024-0015/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan Vol.23, 71-74, by J-STAGE]
<Title:> 分子動力学計算を用いたNa₂O-K₂O-SiO₂系ガラスにおける混合アルカリ効果の再現
<Author(s):> 吉本　直樹, 澤口　直哉
<Corresponding author E-Mill:> nasawa(at)muroran-it.ac.jp
<Abstract:> Molecular dynamics simulation of y{(1-x)Na₂O-xK₂O}-(1-y)SiO₂ glasses used an improved interatomic potential was performed to investigate the mixed alkali effect. The relation of self-diffusion coefficient of potassium and of sodium was improved, but the trend with x of the self-diffusion coefficient of potassium has become worse than the previous work.
<Keywords:> Keywords Molecular dynamics, Silicate glass, Mixed-Alkali Effect
<URL:> https://www.jstage.jst.go.jp/article/jccj/23/3/23_2024-0025/_article/-char/ja/