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相关概念视频

Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.3K
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.
3.3K
Polymers02:34

Polymers

35.4K
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...
35.4K
Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

2.5K
Copolymers are the products obtained from the polymerization of multiple monomer species. So, in a polymer chain itself, there can be multiple repeating units that come from different monomers. The process of synthesizing a polymer from different monomer species is called copolymerization. When two monomers are involved, the polymer is known as a bipolymer. Polymers with three and four monomers are termed terpolymers and quaterpolymers, respectively. Figure 1 depicts the copolymerization of...
2.5K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.2K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
2.2K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

2.8K
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...
2.8K
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

2.7K
Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
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相关实验视频

Updated: Jun 12, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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基于多覆盖持久性 (MCP) 的机器学习用于聚合物性质预测.

Yipeng Zhang1, Cong Shen2, Kelin Xia1

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.

Briefings in bioinformatics
|September 26, 2024
PubMed
概括
此摘要是机器生成的。

一个新的多覆盖持久性 (MCP) 分子表示增强了聚合物性质的预测. 这种由人工智能驱动的方法,使用渐变增强树模型,优于复杂聚合物数据的传统方法.

关键词:
机器学习是机器学习.分子表示的分子表示.多覆盖性持久性多覆盖性持久性聚合物的数据分析分析.

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科学领域:

  • 聚合物科学 聚合物科学
  • 材料 信息学 信息学
  • 计算化学的计算化学

背景情况:

  • 准确的聚合物性能预测对于高效的聚合物设计至关重要.
  • 人工智能 (AI) 模型显示出希望,但在有效的分子表示方面面临挑战.
  • 现有的方法难以捕获复杂的结构和交互数据.

研究的目的:

  • 引入一种基于多覆盖持久性 (MCP) 的新型分子表示和特色化方法.
  • 应用MCP描述符与渐变增强树 (GBT) 模型用于聚合物性质预测.
  • 评估MCP在表征复杂聚合物结构方面的有效性.

主要方法:

  • 开发了多覆盖持久性 (MCP) 用于分子表示,利用Delaunay切片和罗姆波形.
  • 从MCP生成的持久条形码中提取统计特征作为聚合物描述符.
  • 集成的MCP衍生描述符与梯度增强树 (GBT) 模型用于财产预测.

主要成果:

  • 与传统的指纹模型相比,基于MCP的模型显示出更高的性能.
  • 实现了与高级几何深度学习模型相提并论的准确性.
  • 在预测大尺寸单体结构的性质方面表现出特别高的有效性.

结论:

  • MCP为聚合物信息学中的分子表示提供了一个强大的新视角.
  • 基于MCP的方法有效地捕获聚合物中复杂的几何和拓信息.
  • 这种方法对推进人工智能驱动的聚合物设计和分析具有重大潜力.