Evaluating the (dis)similarity of crystalline, disordered and molecular
compounds is a critical step in the development of algorithms to navigate
automatically the configuration space of complex materials. For instance, a
structural similarity metric is crucial for classifying structures, searching
chemical space for better compounds and materials, and driving the next
generation of machine-learning techniques for predicting the stability and
properties of molecules and materials. In the last few years several strategies
have been designed to compare atomic coordination environments. In particular,
the Smooth Overlap of Atomic Positions (SOAP) has emerged as an elegant
framework to obtain translation, rotation and permutation-invariant descriptors
of groups of atoms, driven by the design of various classes of machine-learned
inter-atomic potentials. Here we discuss how one can combine such local
descriptors using a Regularized Entropy Match (REMatch) approach to describe
the similarity of both whole molecular and bulk periodic structures,
introducing powerful metrics that enable the navigation of alchemical and
structural complexity within a unified framework. Furthermore, using this
kernel and a ridge regression method we can predict atomization energies for a
database of small organic molecules with a mean absolute error below 1kcal/mol,
reaching an important milestone in the application of machine-learning
techniques to the evaluation of molecular properties.

本研究介绍了一种使用机器学习技术探索复杂材料配置空间必不可少的结构相似度度量方法 ——SOAP，以及如何使用 REMatch 方法将这些局部描述符相结合，并在小有机分子数据库中达到一个平均绝对误差小于 1 kcal/mol 的预测效果的重要里程碑。