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

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

2.6K
Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
2.6K
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.7K
For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
3.7K

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

AI-Based Polymer Classification Using Ensemble Deep Learning and Heuristic Optimization: Implications for Recycling Applications.

Polymers·2026
Same author

An Artificial Intelligence-Based Melt Flow Rate Prediction Method for Analyzing Polymer Properties.

Polymers·2025
Same author

Data-Driven Polymer Classification Using BiGRU and Hybrid Metaheuristic Optimization Algorithms.

Polymers·2025
Same author

Non-Enzymatic Glucose Sensors Composed of Polyaniline Nanofibers with High Electrochemical Performance.

Molecules (Basel, Switzerland)·2024
Same author

Intelligent Wireless Capsule Endoscopy for the Diagnosis of Gastrointestinal Diseases.

Diagnostics (Basel, Switzerland)·2023
Same author

Electrochemical Sensing of H<sub>2</sub>O<sub>2</sub> by Employing a Flexible Fe<sub>3</sub>O<sub>4</sub>/Graphene/Carbon Cloth as Working Electrode.

Materials (Basel, Switzerland)·2023
Same journal

RETRACTED: Alshabanah et al. Elastic Nanofibrous Membranes for Medical and Personal Protection Applications: Manufacturing, Anti-COVID-19, and Anti-Colistin Resistant Bacteria Evaluation. <i>Polymers</i> 2021, <i>13</i>, 3987.

Polymers·2026
Same journal

Correction: Kang et al. Energy-Saving Electrospinning with a Concentric Teflon-Core Rod Spinneret to Create Medicated Nanofibers. <i>Polymers</i> 2020, <i>12</i>, 2421.

Polymers·2026
Same journal

Influence of Self-Adhesive Resin Composite Deep Marginal Elevation on the Sealing Ability of CAD/CAM Lithium Disilicate Glass-Ceramic Inlays: An In Vitro Study.

Polymers·2026
Same journal

Modulating Exciton Dynamics Through Fluorescent Side Group Incorporation in Benzodithiophene-Benzotriazole-Isoindigo Terpolymers.

Polymers·2026
Same journal

PLA/PBSA Biocomposites Reinforced with Tangerine Tree-Derived Agro-Industrial Waste for Rigid Packaging: Effect of Extraction Treatment on Morphology and Thermo-Mechanical Performance.

Polymers·2026
Same journal

Synergistic Coatings Based on Chitosan and <i>Eugenia caryophyllata</i> Essential Oil to Improve Postharvest Quality of <i>Capsicum chinense</i>.

Polymers·2026
查看所有相关文章

相关实验视频

Updated: Sep 16, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

881

通过帕雷托优化SMILES基于深度学习的高精度聚合物性质检测.

Mohammad Anwar Parvez1, Ibrahim M Mehedi2

  • 1Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Polymers
|July 12, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于聚合物性质分类的新型AI模型,达到98.66%的准确性. 基于使用帕雷托优化算法 (SMILES-PPDCPOA) 检测和分类的简化分子输入线输入系统提供了高效和准确的聚合物信息学.

关键词:
分子输入线输入系统系统.混合深度学习是混合深度学习.线性缩放规范化的规范化帕雷托优化算法的优化算法聚合物特性检测检测的聚合物.

更多相关视频

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform
10:01

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

Published on: September 27, 2016

7.7K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

相关实验视频

Last Updated: Sep 16, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

881
The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform
10:01

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

Published on: September 27, 2016

7.7K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

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
  • 聚合物信息学 聚合物信息学

背景情况:

  • 传统的聚合物设计依赖于直觉和经验,面临着巨大的设计空间和对新材料的需求带来的挑战.
  • 人工智能 (AI),特别是机器学习 (ML) 和深度学习 (DL),为加速材料设计提供了一个有希望的方法.
  • 现有的ML和DL方法显示出潜力,但需要改进的模型来准确地分类聚合物和性能预测.

研究的目的:

  • 设计和开发一种先进的AI模型,使用化学结构输入来对聚合物属性进行分类.
  • 通过创建可扩展和特定领域的解决方案来预测材料特性来增强聚合物信息学.
  • 通过捕捉聚合物结构内的复杂化学依赖,改进现有方法.

主要方法:

  • 基于使用帕雷托优化算法 (SMILES-PPDCPOA) 模型检测和分类聚合物性质的简化分子输入线输入系统的开发.
  • 整合一个一维的卷积神经网络 (1DCNN) 与一个封闭的循环单元 (GRU) 进行特征提取和序列建模.
  • 使用帕雷托优化算法 (POA) 优化1DCNN-GRU模型的超参数,以提高性能.

主要成果:

  • 在八个聚合物性质类别中,SMILES-PPDCPOA模型实现了98.66%的平均分类精度.
  • 该模型展示了高精度和回忆指标,表明了强大的分类性能.
  • SMILES-PPDCPOA表现出卓越的计算效率,在4.97秒内完成任务,超过了GCN-LR和ECFP-NN.N.等既定的方法.

结论:

  • 拟议的SMILES-PPDCPOA模型为聚合物属性分类提供了一种新且有效的深度学习框架.
  • 1DCNN,GRU和帕雷托优化的集成为聚合物信息学提供了可扩展和准确的解决方案.
  • 实验验证证证实了SMILES-PPDCPOA作为推动材料科学和工程的有希望的方法的潜力.