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

The impacts and potential affecting mechanism of pile-driving noise on metabolism of the thick-shell mussel Mytilus coruscus.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Unraveling hidden species diversity of talpid moles using phylogenomics and skull-based deep learning.

Communications biology·2026
Same author

Benzo[a]pyrene induces non-alcoholic fatty liver disease by exacerbating hepatic senescence and disrupting gut-liver axis in zebrafish.

Journal of hazardous materials·2026
Same author

Evaluation and comparison of TCR repertoire differences between patients with cervical cancer and healthy individuals.

Science China. Life sciences·2026
Same author

Transcriptomic insights into aerobic exercise-mediated attenuation of high-fat diet-induced muscle wasting.

Frontiers in physiology·2026
Same author

Knockout of the <i>C4BPA</i> Gene Promotes Mitophagy via Activation of the Pink1/Parkin Pathway and Alleviates the Inflammatory Response by Inhibiting the NF-κB Signalling Pathway in Bovine Mammary Epithelial Cells.

Veterinary sciences·2026
Same journal

Nuclear Gradients from Auxiliary-Field Quantum Monte Carlo and Their Applications in ML-Driven Geometry Optimization and Transition State Search.

Journal of chemical theory and computation·2026
Same journal

Correction to "Cluster-in-Molecule Local Correlation Method with an Accurate Distant Pair Correction for Large Systems".

Journal of chemical theory and computation·2026
Same journal

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy.

Journal of chemical theory and computation·2026
Same journal

Systematic Molecularity-Dependent Entropy Errors in Continuum/RRHO Solution Thermochemistry: Origin and Correction.

Journal of chemical theory and computation·2026
Same journal

After 100 Years of Quantum Mechanics: Toward a Constructive Observation-Centered Perspective.

Journal of chemical theory and computation·2026
Same journal

Sample-Based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds.

Journal of chemical theory and computation·2026
查看所有相关文章

相关实验视频

Updated: May 31, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K

基于注意力的可解释的多尺度图形神经网络用于MOFs.

Lujun Li1,2,3, Haibin Yu2,3, Zhuo Wang2

  • 1Department of Automation, University of Science and Technology of China, Hefei 230026, China.

Journal of chemical theory and computation
|January 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种多尺度晶体图法和MSAIGNN深度学习模型,用于预测金属有机框架 (MOF) 的特性. 该方法通过考虑各种规模的特征并减少冗余的相互作用来提高准确性.

更多相关视频

Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

14.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

相关实验视频

Last Updated: May 31, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K
Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

14.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 金属有机框架 (MOF) 对气体分离和储存具有前景.
  • 图形神经网络 (GNN) 对MOF结构属性预测非常有用.
  • 晶体图需要处理周期性和多尺度特征,与分子图不同.

研究的目的:

  • 开发一种用于构建MOF多尺度晶体图的新方法.
  • 提出一个先进的GNN (MSAIGNN),结合多层次结构信息和注意力机制.
  • 提高MOF属性预测的深度学习模型的准确性和可解释性.

主要方法:

  • 根据原子间相互作用距离,将晶体图分解为子图.
  • 编码单元细胞周期性以捕捉全球晶体结构.
  • 开发了MSAIGNN,结合了三体结合角度和自我注意力图集.

主要成果:

  • 与传统方法相比,MSAIGNN在单元吸附和气体分离方面取得了更高的预测准确度.
  • 该模型有效地学习和利用不同尺度的结构特征,正如注意力得分可视化所证实的那样.
  • 拟议的方法通过将冗余的原子间相互作用的干扰最小化,证明了减少过.

结论:

  • 多尺度晶体图结构和MSAIGNN为MOF属性预测提供了一种高效,多层和可解释的深度学习方法.
  • 这种方法解决了水晶结构和原子间相互作用的复杂性在各种尺度.
  • MSAIGNN为设计和发现新的MOF提供了计算材料科学的重大进步.