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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.03907 |
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| _version_ | 1866917568332169216 |
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| author | Cheng, Jiewei Li, Tingwei Wang, Yongyi Ati, Ahmed H. Sun, Qiang |
| author_facet | Cheng, Jiewei Li, Tingwei Wang, Yongyi Ati, Ahmed H. Sun, Qiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03907 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory Cheng, Jiewei Li, Tingwei Wang, Yongyi Ati, Ahmed H. Sun, Qiang Materials Science 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. |
| title | The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory |
| topic | Materials Science |
| url | https://arxiv.org/abs/2307.03907 |