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

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Jiewei Cheng, Tingwei Li, Yongyi Wang, Ahmed H. Ati, Qiang Sun

Materials Science · Submitted 8 Jul 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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arXiv ID2307.03907 (version 1)
Pith IDY7XSKMDJ, Pith's permanent record of this paper and its review
CitationAhmed H. Ati, Jiewei Cheng, Qiang Sun, Tingwei Li, Yongyi Wang. "The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory." arXiv:2307.03907 (2023). https://pith.science/paper/Y7XSKMDJ
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,297 input tokens.

What the abstract itself states, as probabilities: claims first 0.94 · states limitation 0.06 · human participants 0.01 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.61 · compares with prior results 0.20 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. contribution 0.93 0.88 0.07 asserted 0.96
s1 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. method 1.00 0.96 0.04 asserted 0.99
s2 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. result 0.65 0.96 0.73 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.47 · evidence from simulation or computation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

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