用黎曼流形上的去噪模型实现分子结构优化，能量误差低于1 kcal/mol。
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
- 在物理引导的黎曼流形上构建去噪模型，更贴合分子能量变化规律。
- 在多个数据集上实现化学精度，能量误差低于1 kcal/mol。
- 适合需要高精度分子优化的计算化学与材料科学任务。
我们提出一种基于物理引导黎曼流形（R-DM）的分子结构优化框架。与传统在欧氏空间中操作的方法不同，该方法利用更契合分子能量变化的黎曼度量，从而更稳健地建模势能面。通过引入反映能量特性的内坐标，R-DM在能量误差低于1 kcal/mol的情况下达到化学精度。在QM9、QM7-X和GEOM数据集上的对比实验表明，该方法在结构与能量精度上均优于传统的欧氏空间去噪模型。本方法展示了物理引导坐标在解决复杂分子优化问题中的潜力，对计算化学与材料科学具有重要意义。
原文摘要 · Abstract (English)
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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