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

Polymers02:34

Polymers

40.8K
The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
40.8K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Ogive Graph01:07

Ogive Graph

6.8K
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...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

66
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
66
Bar Graph01:07

Bar Graph

22.0K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
22.0K
Graphs of Functions01:30

Graphs of Functions

321
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
321

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

Universal Interatomic Potentials with DFT for Understanding Orbital Localization in Polydimethylsiloxane-Amorphous Silica Nanocomposites.

ACS omega·2025
Same author

Kinetic, Spectral, and Structural Characterization of a Heme-Containing Peroxidase From the Skin of <i>Cucurbita maxima</i>.

Biochemistry research international·2025
Same author

Accuracy and Adequacy of Preferred Language Data in a Pediatric Electronic Health Record.

Hospital pediatrics·2025
Same author

Accuracy of Race and Ethnicity Data in the Pediatric Electronic Health Record: A Concordance and System Adequacy Study.

Health equity·2025
Same author

Accelerated prediction of molecular properties for per- and polyfluoroalkyl substances using graph neural networks with adjacency-free message passing.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

Transformation of 1D/2D High-Surface-Area Hierarchical Titanium Sulfate Structures to Stable, Morphology-Preserving Titania with Tailored Properties.

Small methods·2025

相关实验视频

Updated: Jan 30, 2026

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers
08:12

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers

Published on: December 16, 2022

3.9K

用于聚合物特性和性质预测的图形神经网络:机遇和挑战

Hector Medina1, Rachel Drake1

  • 1School of Engineering, Liberty University, Lynchburg, Virginia 24515, United States.

Journal of chemical information and modeling
|January 29, 2026
PubMed
概括

机器学习,特别是图形神经网络,加速了聚合物属性预测. 数据短缺等挑战正在通过诸如聚合物技术创新社区资源 (CRIPT) 等倡议得到解决.

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 聚合物科学 聚合物科学

背景情况:

  • 聚合物具有独特的特性,对于储能,轻质材料和生物灵感应用至关重要.
  • 鉴定和预测聚合物特性是具有挑战性的,因为分子复杂性和传统的计算费用.

研究的目的:

  • 审查机器学习的现状,特别是图形神经网络,用于聚合物表征和属性预测.
  • 突出挑战和正在进行的努力,以加速新型聚合物材料的发现.

主要方法:

  • 利用图形神经网络 (GNN) 和相关架构来绘制聚合物结构.
  • 利用机器学习克服密度函数理论和分子动力学等传统方法的局限性.

主要成果:

  • 图形神经网络在加速聚合物的特征和属性预测方面表现有前途.
  • 仍然存在重大挑战,包括需要全面和足够的数据集.

结论:

  • 机器学习在聚合物科学中的应用是一个快速发展的领域,具有巨大的潜力.
  • 合作努力,如CRIPT倡议,对于克服数据限制和推进聚合物创新至关重要.
关键词:
粗粒的 粗粒的数据集数据集数据集.密度函数理论密度函数理论图形神经网络的神经网络机器学习是机器学习.分子动力学分子动力学聚合物表征的聚合物表征房地产预测 房地产预测

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
In situ Photo-rheology Monitors Viscoelastic Changes in Photo-responsive Polymer Networks
07:14

In situ Photo-rheology Monitors Viscoelastic Changes in Photo-responsive Polymer Networks

Published on: June 20, 2025

899

相关实验视频

Last Updated: Jan 30, 2026

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers
08:12

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers

Published on: December 16, 2022

3.9K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
In situ Photo-rheology Monitors Viscoelastic Changes in Photo-responsive Polymer Networks
07:14

In situ Photo-rheology Monitors Viscoelastic Changes in Photo-responsive Polymer Networks

Published on: June 20, 2025

899