论文标题
评估机器学习中常用的各种指纹的结构分辨率
An assessment of the structural resolution of various fingerprints commonly used in machine learning
论文作者
论文摘要
原子环境指纹广泛用于计算材料科学，从机器学习潜力到定量原子构型之间的相似性。已经提出了许多用于构建此类指纹的方法，也称为结构描述符。 In this work, we compare the performance of fingerprints based on the Overlap Matrix(OM), the Smooth Overlap of Atomic Positions (SOAP), Behler-Parrinello atom-centered symmetry functions (ACSF), modified Behler-Parrinello symmetry functions (MBSF) used in the ANI-1ccx potential and the Faber-Christensen-Huang-Lilienfeld (FCHL)指纹在各个方面。我们研究了他们解决本地环境中差异的能力，特别是检查是否有某些原子运动使指纹完全或几乎不变。为此，我们引入了一个灵敏度矩阵，其特征值量化了原子位移模式对指纹的影响。此外，我们检查这些位移是否与局部物理量（例如力）的变化相关。最后，我们将检查扩展到从原子指纹和整个分子的全球量获得的分子指纹之间的相关性。
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called structural descriptors, have been proposed. In this work, we compare the performance of fingerprints based on the Overlap Matrix(OM), the Smooth Overlap of Atomic Positions (SOAP), Behler-Parrinello atom-centered symmetry functions (ACSF), modified Behler-Parrinello symmetry functions (MBSF) used in the ANI-1ccx potential and the Faber-Christensen-Huang-Lilienfeld (FCHL) fingerprint under various aspects. We study their ability to resolve differences in local environments and in particular examine whether there are certain atomic movements that leave the fingerprints exactly or nearly invariant. For this purpose, we introduce a sensitivity matrix whose eigenvalues quantify the effect of atomic displacement modes on the fingerprint. Further, we check whether these displacements correlate with the variation of localized physical quantities such as forces. Finally, we extend our examination to the correlation between molecular fingerprints obtained from the atomic fingerprints and global quantities of entire molecules.
