ArticleACS nano2026
Machine Learning-Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes.
Article in ACS nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Two-dimensional (2D) materials emerge as promising alternatives to conventional polymer-based proton exchange membranes (PEMs) due to their high proton conductivity, mechanical robustness, and surface tunability. Here we present an integrated framework combining ab initio molecular dynamics (AIMD) simulations and machine learning (ML) to accelerate the discovery of proton- and hydrogen-transport properties over 866 nonmetallic 2D materials. Three ML models were trained using AIMD-derived permeation barriers from 488 materials, with Random Forest achieving the highest accuracy and revealing structure-property relationships that govern proton transport. Critical descriptors, including proton-atom distance, pore size, interlayer spacing, and electron affinity, emerged as key predictors of permeation behavior. H
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