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Related Concept Videos

Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

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Unlike small molecules with definite molecular weights, polymers are a mixture of individual polymer chains of varying lengths, each with a unique molecular weight.  So, the molecular weight of a polymer is expressed as an average value based on the average size of the polymer chains. The two most common forms of averages used for polymers are the number average molecular weight and weight average molecular weight.
The number average molecular weight (Mn) is the summation of the number...
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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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ATP and Macromolecule Synthesis01:28

ATP and Macromolecule Synthesis

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Biological macromolecules are organic compounds, predominantly composed of carbon atoms. The carbon atoms are covalently bonded with hydrogen, oxygen, nitrogen, and other minor elements. There are four major biological macromolecule classes: carbohydrates, lipids, proteins, and nucleic acids.
Most macromolecules are composed of single subunits, or building blocks, called monomers. The monomers combine with each other using covalent bonds to form larger molecules known as polymers.
Conversion of...
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Characteristics and Nomenclature of Homopolymers01:00

Characteristics and Nomenclature of Homopolymers

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Polymers that are made up of identical monomer units are called homopolymers. Only one repeating unit is involved in the construction of the homopolymer structure. For example, as depicted in Figure 1, polypropylene is a homopolymer constituted of propylene monomers. Here, the only repeating unit in the polymer chain is propylene.
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Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

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The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the...
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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics.

Christopher Kuenneth1,2, Rampi Ramprasad3

  • 1School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Nature Communications
|July 11, 2023
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We developed a machine-driven polymer informatics pipeline using polyBERT (polymer BERT) and multitask learning to rapidly identify polymers for specific applications. This approach significantly accelerates polymer property prediction, enhancing material discovery.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Polymer Science

Background:

  • Polymers are ubiquitous in daily life, presenting a vast chemical space for material discovery.
  • Identifying application-specific polymers is challenging due to the immense number of possibilities.
  • Current methods for polymer property prediction are often slow and limited in scope.

Purpose of the Study:

  • To present a novel, end-to-end machine-driven polymer informatics pipeline.
  • To enable rapid and accurate searching of the polymer chemical space for suitable candidates.
  • To overcome the limitations of existing polymer property prediction techniques.

Main Methods:

  • Developed polyBERT, a polymer chemical fingerprinting method inspired by Natural Language Processing.
  • Implemented a multitask learning approach to map polyBERT fingerprints to polymer properties.
  • Created a complete machine-driven informatics pipeline for polymer candidate identification.

Main Results:

  • The polyBERT fingerprinting and multitask learning approach significantly accelerates polymer property prediction.
  • The pipeline achieves unprecedented speed (two orders of magnitude faster) compared to handcrafted fingerprint methods.
  • Accuracy is preserved, demonstrating the effectiveness of the machine-driven approach.

Conclusions:

  • The developed polymer informatics pipeline offers a powerful tool for accelerated material discovery.
  • polyBERT and multitask learning provide a scalable and accurate method for predicting polymer properties.
  • This approach is suitable for deployment in large-scale computational architectures, including cloud infrastructures.