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Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-
材料研究開発現場で注目される分子動力学シミュレーション 原子や分子の動きをリアルタイムで追跡できる分子動力学シミュレーションは、近年の計算機性能の飛躍的な向上によって、材料、化学、創薬、バイオなど、さまざまな分野の研究開発において不可欠なツールとなっています。 分子動力学シミュレーションは「圧倒的な分解能をもつ顕微鏡」と形容さ
Molecular dynamics computational chemistry
How to Choose DFT Software: Representative Software by Application and Implementation Steps
In research and development, particularly in material development, challenges arise regarding the time and cost of experiments and securing human resources. In this context, simulation-based approaches using computational chemistry are receiving attention. Specifically, DFT (Density Functional Theory), which calculates electronic states based on quantum mechanics, offers a balance between computational cost and accuracy, and its application in research and development is expanding.
DFT Computational chemistry
An Introduction to Materials Informatics: From Fundamental Concepts to Cutting-Edge Research.
In the fields of materials science and pharmaceutical research and development (R&D), significant challenges persist, including the time, cost, and labor associated with experimentation and evaluation, as well as the difficulty of securing skilled personnel. In particular, traditional R&D methods, which often depend on individual experience and intuition, are reaching their limits in simultaneously achieving both development speed and precision.
MI Computational chemistry
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Molecular Dynamics Simulations for Materials and Molecule Discovery - From Fundamental to Emerging Trends-
Molecular dynamics computational chemistry
NEW
How to Choose DFT Software: Representative Software by Application and Implementation Steps
DFT Computational chemistry
NEW
An Introduction to Materials Informatics: From Fundamental Concepts to Cutting-Edge Research.
MI Computational chemistry
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Application of deep learning to chemical reactions
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GraphNVP
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Chainer Chemistry, a deep learning library for chemistry and biology, is now available
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Accelerating free energy calculations using molecular dynamics with the MN-Core 2 deep learning processor
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Free energy estimation using a diffusion model
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Development of excited state neural network potential
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High-speed X-ray crystallography using PFP
NEW
High-speed crystal structure prediction using PFP and development of its efficient algorithm
NEW
Generalization evaluation of machine learning scoring functions for structure-based virtual screening
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