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

Network Covalent Solids02:18

Network Covalent Solids

13.4K
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...
13.4K
Protein Networks02:26

Protein Networks

2.3K
2.3K
Net Torque Calculations01:19

Net Torque Calculations

9.1K
When a mechanic tries to remove a hex nut with a wrench, it is easier if the force is applied at the farthest end of the wrench handle. The lever arm is the distance from the pivot point (the hex nut in this case) to the person’s hand. If this distance is large, the torque is higher. Only the component of the force perpendicular to the lever arm contributes to the torque. Therefore, pushing the wrench perpendicular to the lever arm is more advantageous. If multiple people apply force to...
9.1K
Mesh Analysis01:20

Mesh Analysis

584
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
584
Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K

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

How the Electrochemical Double Layer Manipulates Molecule-Metal Interactions.

ACS nano·2026
Same author

Beyond Geometric Effects: Particle Size-Dependent Electronic Promotion in Ru Catalysts for Ammonia Synthesis.

Journal of the American Chemical Society·2026
Same author

MoSAIC: Codon Harmonization of Monte Carlo-Based Simulated Annealing for Linked Codons in Heterologous Protein Expression.

ACS synthetic biology·2026
Same author

Operando Cu Aggregation-Induced Spin State Modulation in Fe-Cu Single Atom Catalyst for Enhanced Tandem Electrochemical Nitrate Reduction Reaction.

Journal of the American Chemical Society·2026
Same author

Electric double layer structure in concentrated aqueous solution.

Nature communications·2026
Same author

Thermodynamically consistent incorporation of the Langmuir adsorption model into compressible fluctuating hydrodynamics.

The Journal of chemical physics·2026

相关实验视频

Updated: Jun 13, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

PROFiT-Net:用于材料的属性联网深度学习模型

Se-Jun Kim1, Won June Kim2, Changho Kim3

  • 1Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daehak-ro 291, Yuseong-gu, Daejeon 34141, South Korea.

Journal of the American Chemical Society
|September 12, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习 (DL) 模型PROFiT-Net使用轨道场矩阵准确预测材料特性. 这种人工智能可以加速发现具有有限数据的新功能材料.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

487
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

相关实验视频

Last Updated: Jun 13, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

487
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

科学领域:

  • 材料科学
  • 人工智能
  • 计算化学

背景情况:

  • 准确预测材料属性对于开发新技术至关重要.
  • 现有的材料数据库和深度学习 (DL) 模型面临高可靠性数据的局限性.
  • 开发用于材料科学的先进人工智能需要在稀疏,高质量的数据集上训练模型.

研究的目的:

  • 开发一种新的深度学习模型来预测材料特性.
  • 使用晶体结构表示来提高材料属性预测的准确性.
  • 创建一个能够从有限的高保真材料数据中学习的AI模型.

主要方法:

  • 开发了一个名为PRoperty-networking轨道场maTrix-卷积神经网络 (PROFiT-Net) 的深度学习模型.
  • 使用修改的轨道场矩阵 (OFM) 表示,包含元素属性和价值电子配置.
  • 训练模型以捕捉晶体结构中的元素特性之间的相互关系.

主要成果:

  • 在预测介电常数,实验带间隙和形成度方面,PROFiT-Net取得了很高的准确性.
  • 与其他领先的深度学习模型相比,该模型表现出卓越的性能.
  • PROFiT-Net成功识别了物理模式,避免了非物理预测,并保持了可扩展性.

结论:

  • PROFiT-Net提供了一个可扩展和准确的方法来预测材料属性.
  • 模型从有限的数据中学习的能力解决了材料信息学中的一个关键挑战.
  • 预计PROFiT-Net将大大加速功能性材料的发现和开发.