从简洁的原子表示重建局部环境
Reconstructing local environments from concise atomistic representations
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中文总结 AI 辅助
研究从不变描述符恢复原子结构的逆问题，利用不同相关阶数的紧凑描述符实现准确重建，提供识别描述符近似简并性及恢复不同原子环境的算法手段，还探究了描述符扰动与结构畸变的关系。
中文摘要 AI 辅助
基于对称性的局部原子结构表示，如功率谱或双谱，常用于表征数据集的结构多样性并作为原子机器学习的输入特征。但尚不清楚给定特征能否映射回离散点云、这种重建是否唯一以及描述符的变化如何反映在底层原子几何结构中。本文研究从不变描述符恢复原子结构的逆问题。结果表明，可从不同相关阶数的非常紧凑的描述符获得准确重建，即使是形式上不完整或局部病态的表示也能逆转为准确的几何重建。该重建框架提供了识别不变描述符近似简并性和恢复不同原子环境的通用算法手段。最后，通过从描述符重建原子构型，研究了不同相关阶数的不变描述符中的扰动如何转化为结构畸变。
英文摘要
Symmetry-based representations of local atomic structure, such as the power spectrum or bispectrum, are routinely used to characterize the structural diversity of datasets and as input features for atomistic machine learning. Although these descriptors systematically incorporate increasingly complex geometric correlations, it remains unclear if a given feature can be mapped back to a discrete point cloud, whether such a reconstruction is unique, and how changes in the descriptor are reflected in the underlying atomic geometry. The choice and discretization of the radial and angular bases, as well as the high dimensionality of the resulting feature vectors -- which may contain hundreds or thousands of components -- make this interpretation even more challenging. In this work, we investigate the inverse problem of recovering atomic structures from local invariant descriptors. We show that accurate reconstructions can be obtained from remarkably compact descriptors of different correlation orders, each comprising only a few tens of features. Even representations that are formally incomplete or locally ill-conditioned can be inverted to accurate geometric reconstructions of atomic environments across molecular and material datasets. Our reconstruction framework provides a general algorithmic means of identifying approximate degeneracies of invariant descriptors and recovering distinct atomic environments that cannot be distinguished by a given representation. Finally, by reconstructing atomic configurations from descriptors, we examine how perturbations in invariant descriptors of different correlation orders translate into structural distortions.
