Transferable Learning of Reaction Pathways from Geometric Priors

• URL: http://arxiv.org/abs/2504.15370v1
• Date: Mon, 21 Apr 2025 18:20:53 GMT
• Title: Transferable Learning of Reaction Pathways from Geometric Priors
• Authors: Juno Nam, Miguel Steiner, Max Misterka, Soojung Yang, Avni Singhal, Rafael Gómez-Bombarelli,
• Abstract summary: MEPIN is a scalable machine-learning method for efficiently predicting MEPs from reactant and product geometries.<n>Our method enables the exploration of large chemical reaction spaces with efficient, data-driven predictions of reaction pathways.
• Score: 1.3170830344441016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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