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

Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.6K

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

Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials.

The Journal of chemical physics·2026
Same author

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials.

The Journal of chemical physics·2025
Same author

Moment tensor potential and equivariant tensor network potential with explicit dispersion interactions.

The Journal of chemical physics·2025
Same author

Accelerating structure prediction of molecular crystals using actively trained moment tensor potential.

Physical chemistry chemical physics : PCCP·2025
Same author

MLIP-3: Active learning on atomic environments with moment tensor potentials.

The Journal of chemical physics·2023
Same author

The 2021 room-temperature superconductivity roadmap.

Journal of physics. Condensed matter : an Institute of Physics journal·2021

相关实验视频

Updated: May 13, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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

Published on: April 12, 2019

12.7K

通过使用机器学习的原子间潜力与主动学习加速对吸附物分子位置的全球搜索.

Olga Klimanova1, Nikita Rybin1,2, Alexander Shapeev1,2

  • 1Skolkovo Institute of Science and Technology, Moscow, Russian Federation. alexander@shapeev.com.

Physical chemistry chemical physics : PCCP
|April 15, 2025
PubMed
概括

这项研究引入了一种机器学习算法,以加快在表面上发现分子吸附点的速度. 该方法准确地预测了表面吸附物质的几何形状,并与各种催化系统的文献结果相匹配.

更多相关视频

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

6.3K

相关实验视频

Last Updated: May 13, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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

Published on: April 12, 2019

12.7K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

6.3K

科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 表面科学是一门学科.

背景情况:

  • 确定表面上的分子吸附点对于催化是至关重要的.
  • 准确预测表面吸附物几何形状的计算要求很高.

研究的目的:

  • 开发一种加速算法,用于识别分子吸附部位.
  • 为了提高表面吸附物几何形状的全球优化效率.

主要方法:

  • 利用机器学习的原子间潜力 (瞬间张量潜力) 来近似潜在能量表面.
  • 采用积极学习算法用于自动化培训数据集构建.
  • 在各种催化系统 (CO/Pd111),NO/Pd100,NH3/Cu100,C6H6/Ag111,CH2CO/Rh211) 上验证了方法.

主要成果:

  • 该算法成功加速了对吸附位点的搜索.
  • 预测的表面吸附剂几何学与现有文献数据一致.
  • 这种方法在各种表面结晶学方向和吸附物类型中被证明是有效的.

结论:

  • 开发的算法提供了一种有效和准确的方法来预测分子吸附点.
  • 这种方法可以显著帮助催化和表面科学的研究.
  • 机器学习潜力与主动学习相结合,为计算材料发现提供了强大的工具.