掲載種別：研究論文（学術雑誌）   出版者・発行元：IOP Publishing  

Abstract

Peptoids are biomimetic materials in which the substituent groups are located on the nitrogen atom of the peptide bond, facilitating the formation of nanosheet structures as reported by Mannige et al. (Nature, 526 (2015) 415). Dissipative particle dynamics (DPD) simulations were performed to investigate the aggregation stability of peptoids with different unit lengths. The crucial effective interaction parameters were determined by fragment molecular orbital (FMO) calculations, which allowed to evaluate different molecular interactions (electrostatic and dispersion) in a balanced way. The experimental observation of the unit length dependence was finally reproduced by this FMO-DPD method.

DOI： 10.35848/1347-4065/acf356

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その他リンク： https://iopscience.iop.org/article/10.35848/1347-4065/acf356/pdf

掲載種別：研究論文（学術雑誌）   出版者・発行元：IOP Publishing  

Abstract

We have attempted to use machine learning to streamline the calculation of non-empirical 
parameters for use in dissipative particle dynamics (DPD) simulations. We replaced the 
calculation of molecular interaction energies by the non-empirical molecular orbital method, 
which requires a lot of computational resources, with predictions by machine learning. We 
developed two methods for prediction replacement, which are a 1-step method and a 2-step 
method. The prediction accuracy of the results obtained with these methods was investigated. 
A reduction of about half of the computational cost was expected.

DOI： 10.35848/1347-4065/ace575

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その他リンク： https://iopscience.iop.org/article/10.35848/1347-4065/ace575/pdf

掲載種別：研究論文（学術雑誌）   出版者・発行元：Society of Computer Chemistry Japan  

DOI： 10.2477/jccj.2023-0019

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

Abstract

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress–strain curves for each LCE molecular system. Regression analysis is applied using the stress–strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress–strain curves. To test the predictive performance of the surrogate model, stress–strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.

DOI： 10.1038/s41598-022-23897-0

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その他リンク： https://www.nature.com/articles/s41598-022-23897-0

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society ({ACS})  

DOI： 10.1021/acsomega.1c06587

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society ({ACS})  

DOI： 10.1021/acs.jpca.1c06685

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Wiley  

DOI： 10.1002/jcc.26707

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その他リンク： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/jcc.26707

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：{AIP} Publishing  

DOI： 10.1063/5.0049258

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：AIP Publishing  

DOI： 10.1063/5.0005228

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：The Chemical Society of Japan  

An isolated cluster of 32 ammonia (NH3) molecules was simulated at 50 and 100K by the ab initio fragment molecular orbital based molecular dynamics (FMO-MD) method to analyze the effect of electron correlation via second-order MollerPlesset perturbation (MP2). Both the energy and force of the system were calculated at the MP2 and Hartree-Fock (HF) levels with the 6-31G** basis set. The radial distribution and angular distribution functions were obtained from the trajectories. A comparison of MP2 and HF suggested that the electroncorrelation effect via MP2 leads to relative condensation through electron delocalization, similar to the case of water. Vibrational spectra were also calculated at both the MP2 and HF levels, and the former gave a more minute split of the higher modes. This again suggested that MP2 better describes the electron delocalization.

DOI： 10.1246/bcsj.20190320

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society ({ACS})  

DOI： 10.1021/acschemneuro.9b00602

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掲載種別：研究論文（学術雑誌）  

DOI： 10.7567/1882-0786/ab5e0a

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media LLC  

Abstract

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

DOI： 10.1038/s41598-019-51238-1

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その他リンク： http://www.nature.com/articles/s41598-019-51238-1

記述言語：日本語   出版者・発行元：日本DDS学会  

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記述言語：日本語   出版者・発行元：(公社)日本薬学会  

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出版者・発行元：Royal Society of Chemistry  

DOI： 10.1039/C8RA07428C

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1273/cbij.18.70

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記述言語：中国語   掲載種別：研究論文（学術雑誌）   出版者・発行元：SOC COMPUTER CHEMISTRY, JAPAN  

In order to perform efficient nanoparticle design in drug delivery systems, prediction of physical properties and elucidation of the molecular mechanism are expected by using molecular simulation. In this study, we investigated the molecular mechanism of lipid bilayer formation and mixed lipid vesicle formation using the dissipative particle dynamics (DPD) method and small angle X-ray scattering measurement. Parameters of inter-molecular interaction used for the DPD simulation were estimated in quantum mechanical level by the fragment molecular orbital method (FMO-DPD method). In lipid bilayer formation, it was found that the phospholipid having an unsaturated bond (DOPC) has higher membrane fluidity than the phospholipid (DPPC) having only saturated bonds. In the vesicle formation mixed with phospholipid and positively charged lipid, it was also found that as the proportion of positively charged lipid increases, the fluidity of the membrane increases and the shape changes from a sphere to a flat sphere.

DOI： 10.2477/jccj.2018-0012

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DOI： 10.2477/jccj.2018-0020

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その他リンク： http://orcid.org/0000-0002-4510-5717

掲載種別：研究論文（学術雑誌）   出版者・発行元：SOC COMPUTER CHEMISTRY, JAPAN 2-1-13 HIGASHIUENO, TAITO-KU, TOWA HIGH TOWN UENO 607, TOKYO, 110-0015, JAPAN  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Chem-Bio Informatics Society  

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掲載種別：研究論文（学術雑誌）  

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掲載種別：研究論文（学術雑誌）  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：SOC COMPUTER CHEMISTRY, JAPAN 2-1-13 HIGASHIUENO, TAITO-KU, TOWA HIGH TOWN UENO 607, TOKYO, 110-0015, JAPAN  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：SOC COMPUTER CHEMISTRY, JAPAN 2-1-13 HIGASHIUENO, TAITO-KU, TOWA HIGH TOWN UENO 607, TOKYO, 110-0015, JAPAN  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：American Chemical Society  

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記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：日本コンピュータ化学会  

<p>散逸粒子動力学(DPD: Dissipative Particle Dynamics)法を用いたシミュレーションは生体膜などの幅広い分野で用いられている．従来，このようなDPD シミュレーションでは，バルクの水と界面付近の水を同じように扱う場合がほとんどであった．しかし，こうしたやり方では系の性質を必ずしも忠実に再現しているとはいえない．界面付近の水分子の性質とバルクの水分子の性質がかなり異なったものであるという実験的，あるいは理論的な研究がなされているからである．そこで，我々はこの問題を解決すべく，バルク水と界面水を区別して取り扱うアプローチを提案する．このアプローチを，脂質膜－シリカ－水の系のDPDシミュレーションに対して適用したところ，シリカに脂質膜が吸着する現象をうまくモデル化することができた．</p>

DOI： 10.2477/jccj.2017-0003

CiNii Article

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掲載種別：研究論文（学術雑誌）   出版者・発行元：AIP Publishing  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：North-Holland  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：ACS Publications  

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：The Chemical Society of Japan  

By means of Monte Carlo simulations together with an ab initio molecular orbital method, we present the influence of trimethylamine <i>N</i>-oxide (TMAO), which is a highly polarized spherical-polyhedron, on the distribution and the alignment of surrounding water molecules. The specific alignment of the average dipole moments of solvent water around TMAO is observed in the MC simulation. The number of water molecules in the first hydration shell from MC simulation was in good agreement with the experimental value. A cluster model of TMAO with 12 water molecules was presented as a representative hydration structure of TMAO.

DOI： 10.1246/cl.140043

CiNii Article

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掲載種別：研究論文（学術雑誌）   出版者・発行元：North-Holland  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：The Chemical Society of Japan  

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J-GLOBAL

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J-GLOBAL

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記述言語：日本語   出版者・発行元：日本DDS学会  

J-GLOBAL

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記述言語：日本語   出版者・発行元：(公社)日本薬学会  

J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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出版者・発行元：Springer  

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