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

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

2.9K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
2.9K

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

Silicone-Based Polyurethane for Visual Damage Sensing: The Critical Role of Chemical Bonding in Mechanochromic Soft Materials.

Chemistry, an Asian journal·2026
Same author

Materials Research at Xi'an Jiaotong University: Interdisciplinary Innovation Toward a Sustainable and Intelligent Future.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Dual-Signal Capillary Sensor Based on Viscosity Variation for Bacterial Endotoxin Detection.

Analytical chemistry·2026
Same author

Multifunctional Elastic Supramolecular Hydrogels with Self-Healing, Adhesive, and Ionic Thermoelectric Property for Flexible Sensing and Energy Harvesting.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Combined endovenous ablation with phlebectomy or foam sclerotherapy versus endovenous ablation alone for lower limb varicose veins: A systematic review and meta-analysis.

Phlebology·2026
Same author

Bridging the gap: gradient scaffolds as bioinstructive platforms for peripheral nerve repair.

Chemical Society reviews·2026
Same journal

Synergistic Visible-Light-Driven CO<sub>2</sub> Reduction and H<sub>2</sub>O Oxidation over Ti<sub>3</sub>C<sub>2</sub> Quantum Dot-Modified Cu/g-C<sub>3</sub>N<sub>4</sub> Photocatalysts.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Spontaneous Phase Separation Enables Rapid, Polymerization-Free Fabrication of Gels.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Lamellar-Confinement-Induced ZIF-67 Nanosheet Mixed Matrix Membranes for Enhanced CH<sub>4</sub>/N<sub>2</sub> Separation.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Structure Control of Oblate Nanoparticles Self-Assembled by ABC Cyclic Terpolymers under Soft Confinement.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Tuning Brønsted/Lewis Acid Site Ratios via Ammonia Modulation for Selective Conversion of Glycerol to 1,3-Propanediol or Solketal.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Catalytic and Nitriding Competition of Nitrogen Atom on Graphene and Its Finite Rate Surface Chemistry Model.

Langmuir : the ACS journal of surfaces and colloids·2026
查看所有相关文章

相关实验视频

Updated: Jul 10, 2025

Synthesis of Programmable Main-chain Liquid-crystalline Elastomers Using a Two-stage Thiol-acrylate Reaction
11:17

Synthesis of Programmable Main-chain Liquid-crystalline Elastomers Using a Two-stage Thiol-acrylate Reaction

Published on: January 19, 2016

21.8K

通过基于分子动力学模拟数据的新型机器学习算法预测天然的结晶性.

Qionghai Chen1,2,3, Zhanjie Liu4, Yongdi Huang4

  • 1Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, People's Republic of China.

Langmuir : the ACS journal of surfaces and colloids
|November 20, 2023
PubMed
概括
此摘要是机器生成的。

机器学习增强了天然的分子动力学模拟. 一个新的算法通过分析关键结构因素来预测应变诱导结晶 (SIC),提高模拟速度和准确性.

更多相关视频

The Preparation and Properties of Thermo-reversibly Cross-linked Rubber Via Diels-Alder Chemistry
07:02

The Preparation and Properties of Thermo-reversibly Cross-linked Rubber Via Diels-Alder Chemistry

Published on: August 25, 2016

13.7K
Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture
09:53

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Published on: May 13, 2018

8.4K

相关实验视频

Last Updated: Jul 10, 2025

Synthesis of Programmable Main-chain Liquid-crystalline Elastomers Using a Two-stage Thiol-acrylate Reaction
11:17

Synthesis of Programmable Main-chain Liquid-crystalline Elastomers Using a Two-stage Thiol-acrylate Reaction

Published on: January 19, 2016

21.8K
The Preparation and Properties of Thermo-reversibly Cross-linked Rubber Via Diels-Alder Chemistry
07:02

The Preparation and Properties of Thermo-reversibly Cross-linked Rubber Via Diels-Alder Chemistry

Published on: August 25, 2016

13.7K
Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture
09:53

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Published on: May 13, 2018

8.4K

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
  • 聚合物物理 聚合物物理

背景情况:

  • 由于应变诱导结晶 (SIC),天然 (NR) 具有出色的机械性能.
  • 分子动力学 (MD) 模拟对于在分子水平上研究SIC是有价值的,但在计算上是密集的.
  • 整合机器学习 (ML) 与MD提供了一条加速模拟的途径,同时保持准确性.

研究的目的:

  • 为天然开发基于ML的结晶性算法,根据SIC属性量身定制.
  • 用计算方法研究结构部件对SIC的影响.
  • 为了提高模拟天然中SIC的效率和准确性.

主要方法:

  • 开发了一个使用 eXtreme Gradient Boosting (XGB) 模型的晶度预测算法.
  • 使用生成对抗网络 (GAN) 进行数据增强,以优化有限的训练数据.
  • 利用特征重要性分析和重量整合来分析脂/蛋白质百分比 (ω),键强度 (εH) 和非键强度 (εNH) 对SIC的影响.

主要成果:

  • 开发的ML方法准确地预测NR结晶性.
  • 使用GAN进行数据增强,提高了预测模型的准确性.
  • 特性重要性分析显示,键强度 (εH) 显著影响应变诱导的结晶性,其次是脂/蛋白质百分比 (ω) 和非键强度 (εNH).

结论:

  • 拟议的ML方法,结合XGB和GAN,有效地预测天然中的SIC.
  • 这种方法克服了传统的MD模拟的局限性,提高了速度和准确性.
  • 键强度被确定为影响天然中应变诱导结晶性的最关键因素.