Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929355319410688 |
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| author | El-Machachi, Zakariya Frantzov, Damyan Nijamudheen, A. Zarrouk, Tigany Caro, Miguel A. Deringer, Volker L. |
| author_facet | El-Machachi, Zakariya Frantzov, Damyan Nijamudheen, A. Zarrouk, Tigany Caro, Miguel A. Deringer, Volker L. |
| contents | Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14814 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide El-Machachi, Zakariya Frantzov, Damyan Nijamudheen, A. Zarrouk, Tigany Caro, Miguel A. Deringer, Volker L. Chemical Physics Materials Science Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials. |
| title | Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2405.14814 |