# Machine Learning–Driven Design of Sustainable Polymer Membranes: Integrated Prediction of Gas Permeability, Selectivity, and Biodegradability

## TL;DR

This paper uses machine learning to design sustainable polymer membranes that efficiently separate CO2 and are biodegradable.

## Contribution

A novel machine learning framework predicts gas permeability, selectivity, and biodegradability of polymer membranes.

## Key findings

- Machine learning models accurately predict gas permeability with limited polymer samples.
- Over 500 high-performance polymers exceeding Robeson upper bounds were identified.
- The framework integrates biodegradability prediction into membrane material design.

## Abstract

Increasing CO2 emissions are causing environmental
pollution
worldwide, and there is a growing need for the development of efficient
CO2 separation and recovery technologies. Many polymer
materials, including polymer membranes, are difficult to decompose
once released into the environment, leading to environmental pollution
and adverse effects on ecosystems, and accordingly, biodegradable
materials are required. In this study, we focused on the gas permeability
and biodegradability of polymer materials and developed machine learning
models to explore highly selective CO2 separation polymer
membrane materials and predict their biodegradability. For gas permeability
prediction, we used a data set of polymer membranes with standardized
synthesis and film-forming conditions, confirming that highly accurate
gas permeability coefficient predictions were possible even with a
small number of polymer samples. Furthermore, we predicted the gas
permeability coefficients and gas selectivity of 3219 polymer material
candidates and identified approximately 500 high-performance polymers
that exceed the Robeson upper bounds. Then, the polymers predicted
to have biodegradability were also included. The present machine-learning
framework enables us to propose computational candidates for CO2-separation membranes that are predicted to exhibit both high
gas separation performance and biodegradability within the studied
chemical space, providing hypothesis-generating guidance for future
experimental studies.