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Local atomic environment descriptors (LAEDs) are used in the materialsscience and chemistry communities, for example, for the development of machinelearning interatomic potentials. Despite the fact that LAEDs have beenextensively studied and benchmarked for various applications, global structuredescriptors (GSDs), i.e., descriptors for entire molecules or crystalstructures, have been mostly developed independently based on other approaches.Here, we propose a systematically improvable methodology for constructing aspace of representations of GSDs from LAEDs by incorporating statisticalinformation and information about chemical elements. We apply the method toconstruct GSDs of varying complexity for lithium thiophosphate structures thatare of interest as solid electrolytes and use an information-theoretic approachto obtain an optimally compressed GSD. Finally, we report the performance ofthe compressed GSD for energy prediction tasks.

Constructing and Compressing Global Moment Descriptors from Local Atomic Environments