Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence

TL;DR

This paper presents an AI-driven approach to rapidly design new proton-exchange membranes for electrolyzers, aiming to replace costly Nafion membranes with sustainable alternatives, by generating and screening millions of polymer candidates.

Contribution

The work introduces a virtual synthesis and machine learning framework for designing and evaluating new membranes, significantly accelerating materials discovery for green hydrogen production.

Findings

Validated models against known membranes data

Designed over 1,700 new candidate membranes

Proposed an interactive AI-based materials design scheme

Abstract

Water electrolysis is an eco-friendly method for hydrogen production that has reached significant levels of technological maturity. Among commercialized water-electrolysis technologies, proton-exchange membrane electrolyzers offer high current density, fast dynamic response, and compact system design, among other advantages. On the other hand, managing their high capital cost and the ``forever-chemistry'' nature of Nafion, a perfluorinated proton-exchange membrane widely used in such devices, remains a major challenge. Searches for fluorine-free replacements for Nafion, pursued largely through physical experimentation, have been active for decades with limited success. In this work, we develop and demonstrate an AI-based strategy for designing new proton-exchange membranes for electrolyzers. Two key components of this strategy are an implementation of the virtual forward-synthesis approach and a set of machine-learning predictive models for essential application-inspired membrane properties; the former generates a vast space of millions of synthesizable polymers, which are then evaluated and screened by the latter. The strategy is validated against experimental data for known membranes and then applied to design over 1,700 new synthesizable candidates. This article concludes with a forward-looking vision in which the strategy could be elevated into an interactive and iterative scheme that are based on large language models to facilitate materials design in multiple ways.