Transferable Learning of Reaction Pathways from Geometric Priors
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908837919850496 |
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| author | Nam, Juno Steiner, Miguel Misterka, Max Yang, Soojung Singhal, Avni Gómez-Bombarelli, Rafael |
| author_facet | Nam, Juno Steiner, Miguel Misterka, Max Yang, Soojung Singhal, Avni Gómez-Bombarelli, Rafael |
| contents | Identifying minimum-energy paths (MEPs) is crucial for understanding chemical reaction mechanisms but remains computationally demanding. We introduce MEPIN, a scalable machine-learning method for efficiently predicting MEPs from reactant and product configurations, without relying on transition-state geometries or pre-optimized reaction paths during training. The task is defined as predicting deviations from geometric interpolations along reaction coordinates. We address this task with a continuous reaction path model based on a symmetry-broken equivariant neural network that generates a flexible number of intermediate structures. The model is trained using an energy-based objective, with efficiency enhanced by incorporating geometric priors from geodesic interpolation as initial interpolations or pre-training objectives. Our approach generalizes across diverse chemical reactions and achieves accurate alignment with reference intrinsic reaction coordinates, as demonstrated on various small molecule reactions and [3+2] cycloadditions. Our method enables the exploration of large chemical reaction spaces with efficient, data-driven predictions of reaction pathways. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transferable Learning of Reaction Pathways from Geometric Priors Nam, Juno Steiner, Miguel Misterka, Max Yang, Soojung Singhal, Avni Gómez-Bombarelli, Rafael Chemical Physics Materials Science Machine Learning Identifying minimum-energy paths (MEPs) is crucial for understanding chemical reaction mechanisms but remains computationally demanding. We introduce MEPIN, a scalable machine-learning method for efficiently predicting MEPs from reactant and product configurations, without relying on transition-state geometries or pre-optimized reaction paths during training. The task is defined as predicting deviations from geometric interpolations along reaction coordinates. We address this task with a continuous reaction path model based on a symmetry-broken equivariant neural network that generates a flexible number of intermediate structures. The model is trained using an energy-based objective, with efficiency enhanced by incorporating geometric priors from geodesic interpolation as initial interpolations or pre-training objectives. Our approach generalizes across diverse chemical reactions and achieves accurate alignment with reference intrinsic reaction coordinates, as demonstrated on various small molecule reactions and [3+2] cycloadditions. Our method enables the exploration of large chemical reaction spaces with efficient, data-driven predictions of reaction pathways. |
| title | Transferable Learning of Reaction Pathways from Geometric Priors |
| topic | Chemical Physics Materials Science Machine Learning |
| url | https://arxiv.org/abs/2504.15370 |