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

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

NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements.

Nature computational science·2026
Same author

Rapid Water Diffusion through Extended Networks in MOFs.

Nano letters·2026
Same author

Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials.

Journal of chemical theory and computation·2026
Same author

qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations.

Journal of chemical theory and computation·2026
Same author

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations.

The Journal of chemical physics·2026
Same author

Author Correction: Switching graphitic polytypes in elastically coupled cavities.

Nature nanotechnology·2026

相关实验视频

Updated: Jul 9, 2025

Chemogenetic Regulation in Reprogrammed Stem Cell-derived Precursor Cells in Treating Neurodegenerative Diseases
09:44

Chemogenetic Regulation in Reprogrammed Stem Cell-derived Precursor Cells in Treating Neurodegenerative Diseases

Published on: May 2, 2025

167

将D3分散校正与神经进化机器学习潜力的结合.

Penghua Ying1, Zheyong Fan2

  • 1Department of Physical Chemistry, School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel.

Journal of physics. Condensed matter : an Institute of Physics journal
|December 5, 2023
PubMed
概括

本研究介绍了神经进化潜力与D3校正 (NEP-D3) 模型,以准确模拟材料中的短期和长期相互作用. NEP-D3模型改进了结合能量的描述,并降低了金属有机框架中的导热率.

关键词:
D3分散校正方法 D3分散校正方法在 GPUMD 中使用.两层石墨烯的两个层.机器学习的潜力.金属有机框架的框架.神经进化潜在的潜力导热率 导热率 导热率 导热率 导热率 导热率

更多相关视频

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.5K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

相关实验视频

Last Updated: Jul 9, 2025

Chemogenetic Regulation in Reprogrammed Stem Cell-derived Precursor Cells in Treating Neurodegenerative Diseases
09:44

Chemogenetic Regulation in Reprogrammed Stem Cell-derived Precursor Cells in Treating Neurodegenerative Diseases

Published on: May 2, 2025

167
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.5K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K

科学领域:

  • 计算材料科学 计算材料科学
  • 量子化学是一种量子化学.
  • 凝聚物质物理学 凝聚物质物理学

背景情况:

  • 机器学习潜力 (MLP) 广泛用于原子模拟.
  • 在MLPs中的短切断限制了长距离分散相互作用的准确建模.
  • 精确的分散力建模对于许多材料属性至关重要.

研究的目的:

  • 开发一个统一的模型,将神经进化潜力 (NEP) 与D3分散校正结合起来.
  • 为了使短距离结合和远距离分散相互作用的同时建模.
  • 为了提高材料科学应用的原子模拟的准确性.

主要方法:

  • 将D3分散校正方案集成到神经进化潜力框架中,创建NEP-D3模型.
  • 在gpumd包中实施NEP-D3模型,以实现广泛适用.
  • 通过对双层石墨烯和金属有机框架的模拟进行验证.

主要成果:

  • 与纯NEP相比,NEP-D3模型提供了对双层石墨烯的结合和滑动能量的改进描述.
  • 在三个金属有机框架中,散射相互作用被证明可以将导热率降低约10%.
  • 在gpumd中成功实现了D3校正,支持各种交换相关函数.

结论:

  • 在原子模拟中,NEP-D3模型有效地捕捉了短距离和长距离相互作用.
  • 这种方法为材料建模提供了更准确,更通用的工具.
  • 这些发现对理解和预测材料特性,如导热性等有意义.