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Related Concept Videos

Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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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.
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Polymers02:34

Polymers

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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...
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Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

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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...
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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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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...
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Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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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...
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Polymer Classification: Architecture01:14

Polymer Classification: Architecture

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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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Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.

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
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Summary
This summary is machine-generated.

A novel Multi-Cover Persistence (MCP) molecular representation enhances polymer property prediction. This AI-driven approach, using Gradient Boosting Tree models, outperforms traditional methods for complex polymer data.

Keywords:
machine learningmolecular representationmulti-cover persistencepolymer data analysis

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Area of Science:

  • Polymer Science
  • Materials Informatics
  • Computational Chemistry

Background:

  • Accurate polymer property prediction is vital for efficient polymer design.
  • Artificial intelligence (AI) models show promise but face challenges in effective molecular representation.
  • Existing methods struggle to capture complex structural and interactional data.

Purpose of the Study:

  • To introduce a novel Multi-Cover Persistence (MCP)-based molecular representation and featurization method.
  • To apply MCP descriptors with Gradient Boosting Tree (GBT) models for polymer property prediction.
  • To evaluate the efficacy of MCP in characterizing complex polymer structures.

Main Methods:

  • Developed Multi-Cover Persistence (MCP) for molecular representation, utilizing Delaunay slices and Rhomboid tiling.
  • Extracted statistical features from persistent barcodes generated by MCP as polymer descriptors.
  • Integrated MCP-derived descriptors with Gradient Boosting Tree (GBT) models for property prediction.

Main Results:

  • The MCP-based model demonstrated superior performance compared to traditional fingerprint-based models.
  • Achieved accuracy comparable to advanced geometric deep learning models.
  • Showed particular effectiveness in predicting properties for large-sized monomer structures.

Conclusions:

  • MCP offers a powerful new perspective for molecular representation in polymer informatics.
  • The MCP-based approach effectively captures intricate geometric and topological information in polymers.
  • This method holds significant potential for advancing AI-driven polymer design and analysis.