Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Polymer Classification: Architecture

Polymer Classification: Architecture

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Hydrophilic Co-bonding Strategy of Cu-Cu and Al<sub>2</sub>O<sub>3</sub>-Al<sub>2</sub>O<sub>3</sub> for 3D Integration.

ACS omega·2026

Same author

Stable Analog Weight Programming in Single-Crystalline van der Waals Ferroelectric Transistors for Reliable Computing-in-Memory.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

From Planar to 3D: The Evolution of Complementary Field-Effect Transistor for Next-Generation Semiconductor Nodes.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Synergistic Effects of Ceria Morphology in Magnetically-Assisted Chemical Mechanical Polishing: A Dual-Component Approach for Ultraprecision Surface Planarization.

ACS omega·2026

Same author

Multifunctional polyimide performance prediction based on explainable machine learning.

Smart molecules : open access·2026

Same author

Hierarchical incremental learning deciphers molecular arrangements in multi-component materials.

Nature communications·2025

Search research articles

Related Experiment Video

Updated: Jul 18, 2025

Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems

Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems

Published on: June 23, 2023

Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning.

Han Zhang¹, Haoyuan Li¹, Hanshen Xin¹

¹School of Microelectronics, Shanghai University, Shanghai 201800, China.

Journal of Chemical Information and Modeling

|August 24, 2023

Summary

Machine learning models accurately predict polyimide optical properties and glass transition temperature. Molecular elasticity and atom placement are key factors influencing these properties, aiding flexible display material development.

More Related Videos

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

Published on: February 7, 2017

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Jul 18, 2025

Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems

Author Spotlight: Advances in Nanoscale Infrared Spectroscopy to Explore Multiphase Polymeric Systems

Published on: June 23, 2023

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

Published on: February 7, 2017

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Area of Science:

Materials Science
Computational Chemistry
Polymer Science

Background:

Machine learning accelerates new material development, but optical property prediction for polyimides is underexplored.
Understanding structure-property relationships is crucial for designing advanced polyimide materials.

Purpose of the Study:

To develop machine learning models for predicting polyimide glass transition temperature and cut-off wavelength.
To identify key structural features influencing these properties.

Main Methods:

Collected 652 polyimide molecular structures.
Applied seven machine learning algorithms for prediction tasks.
Extracted feature information from repeating unit structures.

Main Results:

Achieved root mean square errors of 33.92 °C for glass transition temperature (R=0.861) and 17.18 nm for cut-off wavelength (R=0.837).
Identified molecular elasticity as critical for glass transition temperature.
Determined that the presence and location of heterogeneous atoms significantly impact cut-off wavelengths.

Conclusions:

Validated prediction models with synthesized polyimide materials, showing good agreement between experimental and predicted values.
Established a foundation for data-driven polyimide structural design and materials preparation.
Results support the development of polyimides for flexible display applications.