Machine learning enables interpretable discovery of innovative polymers for gas separation membranes.

The article presents a study on the machine learning (ML) implementation for the discovery of innovative polymers for gas separation membranes. It discusses the mechanism and performance of ML models for gas permeability prediction along with chemical insights from the interpretation of ML models. It also analyzes the discovery of high-performance polymers and validates it using Molecular dynamics (MD) simulations.