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Machine Learning-Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes
Yuting Li1,2, Daniel Bahamon1,2, Marcelo Lozada-Hidalgo3
1Research & Innovation Center for Graphene and 2D Materials (RIC-2D), Khalifa University of Science and Technology, Abu Dhabi 127788, UAE.
ACS Nano
|December 27, 2025
Summary
This study introduces a framework using simulations and machine learning to discover new 2D materials for proton exchange membranes (PEMs). It identifies promising materials for efficient hydrogen transport and energy technologies.
Area of Science:
- Materials Science
- Computational Chemistry
- Nanotechnology
Background:
- Conventional polymer-based proton exchange membranes (PEMs) face limitations.
- Two-dimensional (2D) materials offer superior proton conductivity, mechanical strength, and tunable surfaces.
- There is a need for accelerated discovery of novel 2D materials for advanced PEM applications.
Purpose of the Study:
- To develop an integrated computational framework for discovering proton- and hydrogen-transport properties in nonmetallic 2D materials.
- To identify key structure-property relationships governing proton transport.
- To guide the design of high-performance nanomaterials for hydrogen energy technologies.
Main Methods:
- Utilized ab initio molecular dynamics (AIMD) simulations to calculate permeation barriers.
- Employed machine learning (ML), specifically Random Forest, to predict proton transport properties.
- Screened 866 nonmetallic 2D materials and performed additional AIMD for selectivity analysis.
Main Results:
- Identified critical descriptors for proton transport, including proton-atom distance, pore size, interlayer spacing, and electron affinity.
- Discovered 18 promising 2D materials for H+/H2 selectivity, including novel candidates like 2D Si, Ge, TeC, TeCl, GeSe, and CSe.
- Validated the framework's robustness by identifying known materials like graphene and hexagonal boron nitride.
Conclusions:
- The integrated AIMD-ML framework effectively accelerates the discovery of 2D materials for PEMs.
- Several experimentally synthesized but underexplored 2D materials show significant potential for proton conduction.
- This work provides fundamental insights and practical guidance for designing next-generation nanomaterials for hydrogen energy.
