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

Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

26.3K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
26.3K

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

Data-driven prediction of ionic conductivity in solid-state electrolytes with machine learning and large language models.

The Journal of chemical physics·2026
Same author

A Master Isotherm Model Approach to Quantify Defects in UiO-66 from Nitrogen Adsorption Isotherms.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks.

Journal of the American Chemical Society·2025
Same author

Correction to "Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF6 Replacement Gases".

The journal of physical chemistry. A·2025
Same author

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models.

ACS applied materials & interfaces·2024
Same author

Correction to "PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks".

Journal of chemical theory and computation·2024

相关实验视频

Updated: Jun 24, 2025

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates
08:07

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates

Published on: June 18, 2013

15.0K

帕克曼:基于晶体图卷积网络的纳米孔材料的强大的部分原子电荷预测器.

Guobin Zhao1, Yongchul G Chung1

  • 1School of Chemical Engineering, Pusan National University, Busan 46241, South Korea.

Journal of chemical theory and computation
|June 1, 2024
PubMed
概括

我们开发了PACMAN,这是一种快速的图形卷积网络方法,用于在金属有机框架 (MOF) 和共价有机框架 (COF) 中赋值原子电荷. 这种工具可以在几秒钟内准确地充电纳米孔状材料,包括含有离子的材料.

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 纳米技术纳米技术

背景情况:

  • 准确的部分原子电荷对于预测诸如金属有机框架 (MOF) 和共价有机框架 (COF) 等多孔材料的行为至关重要.
  • 计算这些电荷的现有方法可能在计算上昂贵,或者对复杂的系统缺乏准确性,特别是那些含有离子的系统.

研究的目的:

  • 开发一种快速而准确的计算方法,在MOF和COF晶体结构上分配部分原子电荷.
  • 通过基于机器学习的充电赋值,可靠地预测材料特性,如气体吸收和吸附.

主要方法:

  • 使用图形卷积网络 (GCNs) 来自量子金属有机框架 (QMOF) 数据库的大型数据集 (>180万个数据点) 上进行训练.
  • 开发了使用机器学习网络 (PACMAN) 模型预测原子电荷,并与已建立的电荷计算方法 (DDEC6,Bader,CM5) 进行了验证.
  • 通过使用大法典蒙特卡洛 (GCMC) 对CO2和N2吸收和Widom粒子插入水恒定的模拟来评估模型的性能. 亨利定律常数.

主要成果:

  • 帕克曼模型实现了高精度,测试组的平均绝对误差 (MAE) 为0.0055e.
  • 在各种MOF和COF化学和拓中展示了一致的电荷分配,超过了以前的机器学习模型.
  • 成功地为含有离子的纳米孔质材料分配了部分原子电荷,这是以前的ML方法缺乏的能力.

更多相关视频

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K
High Resolution Physical Characterization of Single Metallic Nanoparticles
09:56

High Resolution Physical Characterization of Single Metallic Nanoparticles

Published on: June 28, 2019

5.8K

相关实验视频

Last Updated: Jun 24, 2025

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates
08:07

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates

Published on: June 18, 2013

15.0K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.8K
High Resolution Physical Characterization of Single Metallic Nanoparticles
09:56

High Resolution Physical Characterization of Single Metallic Nanoparticles

Published on: June 28, 2019

5.8K
  • 帕克曼计算显示,在预测CO2,N2和水吸附性质方面,与DDEC6电荷有很好的一致性.
  • 对于高达500个原子的结构,实现了不到10秒的运行时间.

    • 结论:

    • 在MOF和COF中,PACMAN提供了一种快速,准确和多用途的方法,用于在MOF和COF中分配部分原子电荷.
    • 该模型处理含离子材料的能力及其速度使其成为材料发现和属性预测的宝贵工具.
    • 一个可访问的网络接口是可用的,促进广泛采用和加速纳米孔状材料的研究.