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.

我们提出了一种有效的方法来消除热振动，该方法使凝聚态原子模拟中的复杂动力学分析任务变得复杂。我们的方法使用在合成噪声但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。由此产生的去噪结构清楚地揭示了潜在的晶体顺序，同时保留了与晶体缺陷相关的无序性。我们的降噪器是纯几何的，与原子间势无关，并且在没有显式模拟输入的情况下进行训练，可以应用于从截然不同的原子间相互作用生成的模拟数据。该降噪器可以改进现有的分类方法，例如公共邻域分析和多面体模板匹配，在最新的热扰动结构直至熔点的基准数据集上达到完美的分类精度。此处在各种原子模拟环境中进行了演示，该降噪器具有通用性、鲁棒性，并且易于扩展以描绘结构和化学复杂材料中的无序与有序。