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

journal of chemical physics online/the journal of chemical physics/journal of chemical physics

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