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arXiv · 2411.19769

Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy

Jeheon Woo·Seonghwan Kim·Jun Hyeong Kim·Woo Youn Kim
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Abstract

We introduce a framework for molecular structure optimization using denoising model on a physics-informed Riemannian manifold (R-DM). Unlike conventional approaches operating in Euclidean space, our method leverages a Riemannian metric that better aligns with molecular energy change, enabling more robust modeling of potential energy surfaces. By incorporating internal coordinates reflective of energetic properties, R-DM achieves chemical accuracy with an energy error below 1 kcal/mol. Comparative evaluations on QM9, QM7-X, and GEOM datasets demonstrate improvements in both structural and energetic accuracy, surpassing conventional Euclidean-based denoising models. This approach highlights the potential of physics-informed coordinates for tackling complex molecular optimization problems, with implications for tasks in computational chemistry and materials science.

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Jeheon Woo, Seonghwan Kim, Jun Hyeong Kim, Woo Youn Kim. 2024-11-29. Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy. https://arxiv.org/abs/2411.19769

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