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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.

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Updated: Jul 5, 2025

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

MLatom 3:一个机器学习增强的计算化学模拟和工作流程的平台.

Pavlo O Dral^1,2, Fuchun Ge^1,2, Yi-Fan Hou^1,2

¹State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.

Journal of chemical theory and computation

|January 25, 2024

概括

MLatom 3是一个开源软件包,增强了计算化学模拟. 它可以为分子动力学,光谱模拟和使用机器学习 (ML) 和量子力学进行属性计算提供自定义的工作流.

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

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Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

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相关实验视频

Last Updated: Jul 5, 2025

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

科学领域:

计算化学计算化学
机器学习应用机器学习应用

背景情况:

机器学习 (ML) 是计算化学中的一个不断增长的工具.
快速的ML开发需要灵活的软件来实现自定义的工作流程.

研究的目的:

介绍MLatom 3,一个用于计算化学的开源包.
使用户能够利用ML进行增强的模拟和复杂的工作流程.

主要方法:

MLatom 3提供命令行,输入文件和Python脚本选项.
支持本地机器和XACS云计算服务上的模拟.
与外部软件和库进行集成,以提高灵活性.

主要成果:

能够计算能量,热化学性质和几何优化.
有助于分子动力学,量子动力学和光谱模拟 (振动,UV/vis,TPA).
提供预训练的ML模型 (例如AIQM1),并允许自定义模型开发.

结论:

MLatom 3提供了一个灵活的,开源的计算化学框架.
它使用户能够使用ML和量子力学方法进行各种模拟.
广泛的接口增强了它的适应性和集成能力.