We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

翻译：我们提出了一种有效的方法，用于去除凝聚态物质原子模拟中使复杂动力学分析变得困难的热振动。该方法利用在合成噪声但其他方面完美的晶格上训练的去噪得分函数，迭代地减去原子位置中的热噪声或扰动。得到的去噪结构清晰地揭示了潜在的晶体有序性，同时保留了与晶体缺陷相关的无序性。我们的去噪器纯粹基于几何，不依赖于原子间势，且无需通过显式模拟输入训练，因此可应用于来自截然不同原子间相互作用的模拟数据。实验表明，该去噪器能够改进现有分类方法（如共同近邻分析和多面体模板匹配），在近期一个热扰动结构基准数据集上达到熔点附近的完美分类精度。在多种原子模拟情境中验证后，该去噪器具有通用性、鲁棒性，并可轻易扩展以区分结构及化学复杂材料中的有序与无序。