Constructing and Compressing Global Moment Descriptors from Local Atomic EnvironmentsDownload PDF

Published: 17 Mar 2023, Last Modified: 21 Apr 2023ml4materials-iclr2023 PosterReaders: Everyone
Keywords: Information content, Dimensionality reduction, Global representations, Local atomic environments, Statistical moments
TL;DR: Constructing global structure descriptors from local atomic environments with mathematical moments and compressing them with an information-theoretic approach
Abstract: Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potentials. Despite the fact that LAEDs have been extensively studied and benchmarked for various applications, global structure descriptors (GSDs), i.e., descriptors for entire molecules or crystal structures, have been mostly developed independently based on other approaches. Here, we propose a systematically improvable methodology for constructing a space of representations of GSDs from LAEDs by incorporating statistical information and information about chemical elements. We apply the method to construct GSDs of varying complexity for lithium thiophosphate structures that are of interest as solid electrolytes and use an information-theoretic approach to obtain an optimally compressed GSD. Finally, we report the performance of the compressed GSD for energy prediction tasks.
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