Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Molecular Models02:00

Molecular Models

37.8K
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.
37.8K

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

Predicting First-in-Human Pharmacokinetics: Comparative Evaluation of Standard PBPK, High-Throughput PBPK, and Machine Learning.

Molecular pharmaceutics·2026
Same author

Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.

Journal of chemical theory and computation·2026
Same author

Toward Quantitative Reaction Dynamics of O<sub>3</sub>.

The journal of physical chemistry letters·2026
Same author

Towards robust foundation models for digital pathology.

Nature communications·2026
Same author

Reaction Pathway Dynamics for Atmospheric Decomposition Reactions: Unimolecular Dissociation of H<sub>2</sub>COO.

The journal of physical chemistry letters·2026
Same author

A supercharged molecular motor operating by constitutional alteration and proton transfer.

Nature chemistry·2026

相关实验视频

Updated: May 28, 2025

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy
06:54

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy

Published on: January 20, 2023

2.1K

对分子,材料和接口的撞击测试机器学习力场:TEA挑战2023中的模型分析.

Igor Poltavsky1, Anton Charkin-Gorbulin1,2, Mirela Puleva1,3

  • 1Department of Physics and Materials Science, University of Luxembourg L-1511 Luxembourg Luxembourg alexandre.tkatchenko@uni.lu igor.poltavskyi@uni.lu.

Chemical science
|February 12, 2025
PubMed
概括

在TEA挑战2023中,评估了用于原子模拟的机器学习力场 (MLFF). 结果显示,MLFF提供了准确性和效率,但需要在各种应用中进行仔细验证.

更多相关视频

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.1K
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

11.8K

相关实验视频

Last Updated: May 28, 2025

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy
06:54

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy

Published on: January 20, 2023

2.1K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.1K
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

11.8K

科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 化学物理 化学物理

背景情况:

  • 原子模拟对于理解分子和材料行为至关重要.
  • 力量场 (FFs) 是模拟的核心,但在准确性和范围方面面临挑战.
  • 在量子力学数据上训练的机器学习力场 (MLFF) 显示出高精度和效率的前景.

研究的目的:

  • 在TEA挑战2023中严格评估常用的MLFF.
  • 评估MLFF在各种应用中的性能,包括潜在的能量表面再现,数据处理,多组件系统和周期结构.
  • 在分子动力学模拟中分析各种MLFF架构的准确性,稳定性和效率.

主要方法:

  • 参与者使用提供的量子力学数据集训练了MLFF模型.
  • "TEA挑战2023"涉及对MLFF在预定义任务上的表现进行系统分析.
  • 在分子动力学模拟中评估的架构包括MACE,SO3krates,sGDML,SOAP/GAP和FCHL19*.

主要成果:

  • 2023年TEA挑战突出了当前MLFF的优点和弱点.
  • 在不同的MLFF架构和应用类型中,性能各不相同.
  • 分析的重点是准确性 (小于kcal mol−1 Å−1),稳定性和计算效率.

结论:

  • 飞机在准确和高效的原子模拟方面具有显著的潜力.
  • 跨不同化学空间和系统类型的验证对于可靠的MLFF应用至关重要.
  • 需要进一步开发来解决局限性并提高MLFFs的通用性.