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Polymers: Molecular Weight Distribution

Polymers: Molecular Weight Distribution

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.

Molecular Weight of Step-Growth Polymers

Molecular Weight of Step-Growth Polymers

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...

Step-Growth Polymerization: Overview

Step-Growth Polymerization: Overview

Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...

Polymers

Polymers

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...

Polymers

Polymers

Ziegler–Natta Chain-Growth Polymerization: Overview

Ziegler–Natta Chain-Growth Polymerization: Overview

Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...

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Updated: Oct 24, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Data-driven algorithms for inverse design of polymers.

Kianoosh Sattari¹, Yunchao Xie, Jian Lin

¹Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65211, USA. yxpx3@mail.missouri.edu linjian@missouri.edu.

|August 16, 2021

Summary

This summary is machine-generated.

Data-driven inverse design accelerates the discovery of novel polymers with desired properties. This review covers polymer representation, screening, optimization, and generative models for efficient chemical space exploration.

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Area of Science:

Materials Science
Computational Chemistry
Polymer Science

Background:

The demand for advanced polymers necessitates efficient exploration of chemical space.
Materials informatics and data-driven approaches are revolutionizing polymer design.
Inverse design strategies offer a powerful platform for targeted polymer development.

Purpose of the Study:

To review advancements in polymer representation for inverse design.
To systematically introduce data-driven inverse design strategies for polymers.
To discuss challenges and opportunities in data-driven polymer design.

Main Methods:

Summarizing progress in polymer representation techniques.
Introducing high-throughput virtual screening for polymer discovery.
Detailing global optimization and generative models in polymer inverse design.

Main Results:

Polymer representation is a crucial prerequisite for effective inverse design.
Data-driven strategies like virtual screening, global optimization, and generative models are key.
These methods enable efficient navigation of chemical space for optimal polymer properties.

Conclusions:

Data-driven inverse design is pivotal for accelerating the discovery of novel polymers.
Further development in algorithms and data handling will enhance polymer design capabilities.
This approach holds significant promise for creating polymers with superior, tailored properties.