AI powered, automated discovery of polymer membranes for carbon capture

TL;DR

This paper introduces an AI-driven, automated framework for discovering polymer membranes optimized for carbon capture, integrating meso-scale validation and enabling rapid computational screening of hundreds of candidates.

Contribution

It presents the first automated inverse design process for complex amorphous materials, combining data generation, generative design, and molecular dynamics validation at meso-scale.

Findings

Validated hundreds of polymer candidates for CO2 filtration

Achieved quantitative agreement in permeability simulations

Reduced discovery-to-validation time to about 100 hours

Abstract

The generation of molecules with Artificial Intelligence (AI) is poised to revolutionize materials discovery. Potential applications range from development of potent drugs to efficient carbon capture and separation technologies. However, existing computational frameworks lack automated training data creation and physical performance validation at meso-scale where complex properties of amorphous materials emerge. The methodological gaps have so far limited AI design to small-molecule applications. Here, we report the first automated discovery of complex materials through inverse molecular design which is informed by meso-scale target features and process figures-of-merit. We have entered the new discovery regime by computationally generating and validating hundreds of polymer candidates designed for application in post-combustion carbon dioxide filtration. Specifically, we have validated each discovery step, from training dataset creation, via graph-based generative design of optimized monomer units, to molecular dynamics simulation of gas permeation through the polymer membranes. For the latter, we have devised a Representative Elementary Volume (REV) enabling permeability simulations at about 1,000x the volume of an individual, AI-generated monomer, obtaining quantitative agreement. The discovery-to-validation time per polymer candidate is on the order of 100 hours in a standard computing environment, offering a computational screening alternative prior to lab validation.