Machine learning models for atom-diatom reactions across isotopologues
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914854896402432 |
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| author | Julian, Daniel Koots, Rian Pérez-Ríos, Jesùs |
| author_facet | Julian, Daniel Koots, Rian Pérez-Ríos, Jesùs |
| contents | This work shows that feed-forward neural networks can predict the final ro-vibrational state distributions of inelastic and reactive processes of the reaction of Ca $+$ H2 $\rightarrow$ CaH $+$ H in the hyperthermal regime, relevant for buffer gas chemistry. Furthermore, these models can be extended to the isotopologues of the reaction involving deuterium and tritium. In addition, we develop a neural network model that can learn across the chemical space based on the isotopologues of hydrogen. The model can predict the outcome of a reaction whose reactants have never been seen. This is done by training on the Ca $+$ H2 and Ca $+$ T2 reactions and subsequently predicting the Ca $+$ D2 reaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01485 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning models for atom-diatom reactions across isotopologues Julian, Daniel Koots, Rian Pérez-Ríos, Jesùs Chemical Physics Computational Physics This work shows that feed-forward neural networks can predict the final ro-vibrational state distributions of inelastic and reactive processes of the reaction of Ca $+$ H2 $\rightarrow$ CaH $+$ H in the hyperthermal regime, relevant for buffer gas chemistry. Furthermore, these models can be extended to the isotopologues of the reaction involving deuterium and tritium. In addition, we develop a neural network model that can learn across the chemical space based on the isotopologues of hydrogen. The model can predict the outcome of a reaction whose reactants have never been seen. This is done by training on the Ca $+$ H2 and Ca $+$ T2 reactions and subsequently predicting the Ca $+$ D2 reaction. |
| title | Machine learning models for atom-diatom reactions across isotopologues |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2407.01485 |