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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.0K
VSEPR Theory for Determination of Electron Pair Geometries
34.0K
Network Covalent Solids02:18

Network Covalent Solids

13.3K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.3K
Neuron Structure01:30

Neuron Structure

12.6K
Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to...
12.6K
Ogive Graph01:07

Ogive Graph

5.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.6K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.8K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.8K
Neural Circuits01:25

Neural Circuits

1.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.0K

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

Orthorhombic B<sub>8</sub>: A Boron Allotrope with Coexisting Superhard and Superconducting Properties.

Inorganic chemistry·2026
Same author

Ambient-pressure superconductivity in covalent Au-B frameworks stabilized by electropositive metals.

Communications chemistry·2025
Same author

Double-core nanothread formation from α-furil <i>via</i> a pressure-induced planarization pathway.

Chemical science·2025
Same author

Al<sub>2</sub>B<sub>12</sub>C with High Ambipolar Mobility Driven by a Unique B-C Framework.

Journal of the American Chemical Society·2024
Same author

Superconductivity in Diamond-Like BC<sub>15</sub>.

Inorganic chemistry·2024
Same author

A Sodium Germanosilicide with Unusual Network Topology.

Journal of the American Chemical Society·2024
Same journal

Solid-State NMR Quantification of Brønsted-Lewis Acid Site Cooperativity in Zeolites for Glucose Conversion.

The journal of physical chemistry letters·2026
Same journal

Ion-Pairing-Mediated Selective Transport of Rare Earth Elements through Functionalized Graphene Nanopores.

The journal of physical chemistry letters·2026
Same journal

Ligand-Tuned CISS-Effect of Atomically Precise Metal Oxido Nanoclusters.

The journal of physical chemistry letters·2026
Same journal

Data-Driven Exploration of the Polyethylene Catalyst Chemical Space via Machine Learning.

The journal of physical chemistry letters·2026
Same journal

Role of Ultrafast Electron-Thermal-Phonon Interactions in High Harmonic Generation and Dephasing from Graphene.

The journal of physical chemistry letters·2026
Same journal

Real-Time Vibrational Spectroscopy Reveals an Inversion Transition State in the Photoisomerization of Phenylazoimidazole.

The journal of physical chemistry letters·2026
查看所有相关文章

相关实验视频

Updated: Jun 12, 2026

Strain Sensing Based on Multiscale Composite Materials Reinforced with Graphene Nanoplatelets
09:38

Strain Sensing Based on Multiscale Composite Materials Reinforced with Graphene Nanoplatelets

Published on: November 7, 2016

EOSnet:嵌入式重叠结构用于图形神经网络,用于预测材料属性.

Shuo Tao1, Li Zhu1

  • 1Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.

The journal of physical chemistry letters
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

新的图形神经网络 (GNN) 模型EOSnet使用高斯重叠矩阵 (GOM) 指纹来捕捉复杂的原子相互作用,以准确地预测材料属性. 这种方法增强了GNN,以更快地发现材料.

更多相关视频

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

相关实验视频

Last Updated: Jun 12, 2026

Strain Sensing Based on Multiscale Composite Materials Reinforced with Graphene Nanoplatelets
09:38

Strain Sensing Based on Multiscale Composite Materials Reinforced with Graphene Nanoplatelets

Published on: November 7, 2016

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

科学领域:

  • 计算材料科学科学 计算材料科学
  • 对于材料的机器学习

背景情况:

  • 图形神经网络 (GNN) 越来越多地用于预测材料属性.
  • 目前的GNN经常难以捕捉多体相互作用,需要手动功能工程.

研究的目的:

  • 介绍EOSnet,一种新的GNN架构.
  • 为了解决捕获多体相互作用的局限性,并减少材料科学用GNN中的手动特征工程.

主要方法:

  • 开发了EOSnet,将高斯重叠矩阵 (GOM) 指纹集成为GNN中的节点特征.
  • GOM指纹通过轨道重叠矩阵编码多体相互作用,提供一个旋转不变的表示.

主要成果:

  • 在预测各种材料特性方面,EOSnet取得了卓越的性能,特别是那些对多体相互作用敏感的材料.
  • 实现了0.163 eV的平均绝对误差用于带隙预测,超过了最先进的模型.
  • 在金属/非金属分类中证明了97.7%的准确性,并在预测机械性质方面表现出色.

结论:

  • 将GOM指纹嵌入到GNN节点特征中显著提高了模型捕获复杂原子相互作用的能力.
  • 通过机器学习,EOSnet代表了材料发现和属性预测的强大进步.