Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Planar rotor-enabled quenching-resistant NIR-II fluorophores for high-contrast bioimaging and efficient cancer phototheranostics.

Materials horizons·2026

Same author

Generalization of long-range machine learning potentials in complex chemical spaces.

Digital discovery·2026

Same author

Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling.

ACS nano·2026

Same author

Boltz-ABFE: Free Energy Perturbation without Crystal Structures.

Journal of chemical theory and computation·2026

Same author

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.

Journal of chemical information and modeling·2026

Same author

Achieving all-atom molecular dynamics accuracy from the Poisson-Boltzmann method through machine learning.

The Journal of chemical physics·2026

Same journal

Continuous Information Descriptors for Electron Localization: Relativistic Spatial Responses, Nonadditivity, and Chemical Bonding.

Journal of chemical theory and computation·2026

Same journal

Determining Quantum Mechanical Methods Suitable for Quantitative Modeling of Hydrogen Atom Transfer by Halogen Atoms.

Journal of chemical theory and computation·2026

Same journal

Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics.

Journal of chemical theory and computation·2026

Same journal

Connection between <i>GW</i> and Extended Coupled Cluster.

Journal of chemical theory and computation·2026

Same journal

Resolving Local and Global Conformational Heterogeneity of the Human Intrinsically Disordered Proteome.

Journal of chemical theory and computation·2026

Same journal

Molecular Modeling of Surfactant Interaction on Phospholipid Bilayers Mimicking Corneal Epithelium.

Journal of chemical theory and computation·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: Sep 14, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

化学列车部署:在百万原子MD模拟中实现机器学习潜力的并行和可扩展的框架.

Paul Fuchs¹, Weilong Chen¹, Stephan Thaler²

¹Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany.

Journal of chemical theory and computation

|July 23, 2025

概括

此摘要是机器生成的。

化学列车部署使LAMMPS中的模型不可知的机器学习潜力 (MLP) 能够在多个GPU上进行高效的大规模分子动力学 (MD) 模拟. 这个框架支持各种JAX定义的潜力,并实现复杂系统的最新性能.

更多相关视频

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

相关实验视频

Last Updated: Sep 14, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

科学领域:

计算化学计算化学
材料科学材料科学材料科学
机器学习机器学习

背景情况:

机器学习潜力 (MLP) 正在彻底改变分子动力学 (MD) 模拟.
现有的MLP软件往往缺乏灵活性,与标准MD包集成,以及GPU并行.
需要一个多功能框架,在既有模拟工具中部署各种MLP.

研究的目的:

引入chemtrain-deploy,这是一个新的框架,用于在LAMMPS中对MLP的模型无关部署.
为了实现大规模的,高性能的MD模拟,使用各种JAX定义的半语言潜力.
通过不同的ML架构和系统验证框架的效率和可扩展性.

主要方法:

开发了chemtrain-deploy,一个框架,将任何JAX定义的半局部潜力与LAMMPS集成在一起.
实现了GPU并行化,以在多GPU系统上进行高效的计算.
在各种系统上使用图形神经网络架构 (MACE,Allegro,PaiNN) 验证了性能和可扩展性.

主要成果:

化学火车部署证明了最先进的效率和可扩展性,用于高达数百万原子的系统.
该框架成功地在LAMMPS中部署和验证了各种MLP (MACE,Allegro,PaiNN).
在各种模拟中证实了性能,包括液体-蒸汽接口,晶体材料和化.

结论:

化学列车部署为在LAMMPS中部署各种MLP提供了实用和高效的解决方案.
该框架促进了高性能,大规模的MD模拟,推进了材料科学和计算化学.
结果为选择和设计MD模拟的未来MLP架构提供了指导.