The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory

TL;DR

This study combines DFT calculations, graph neural networks, and RVB theory to analyze hydrogen adsorption on MXenes, revealing a quantitative relationship between H2 bond length and adsorption distance, aiding hydrogen storage material design.

Contribution

It introduces a computational workflow integrating DFT, machine learning, and RVB theory to predict and understand hydrogen adsorption on a large MXene dataset, which is novel.

Findings

Identified a quantitative relation between H2 bond length and adsorption distance.

Developed a formula based on RVB theory to fit the adsorption data.

Revealed the influence of transition metal ligancy and valence on H2 activation.

Abstract

Motivated by the recent experimental study on hydrogen storage in MXene multilayers [Nature Nanotechnol. 2021, 16, 331], for the first time we propose a workflow to computationally screen 23,857 compounds of MXene to explore the general relation between the activated H2 bond length and adsorption distance. By using density functional theory (DFT), we generate a dataset to investigate the adsorption geometries of hydrogen on MXenes, based on which we train physics-informed atomistic line graph neural networks (ALIGNNs) to predict adsorption parameters. To fit the results, we further derived a formula that quantitatively reproduces the dependence of H2 bond length on the adsorption distance from MXenes within the framework of Pauling's resonating valence bond (RVB) theory, revealing the impact of transition metal's ligancy and valence on activating dihydrogen in H2 storage.