Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials

2025/11/20 by Natascia L. Fragapane, Volker L. Deringer, Fragapane, Natascia L. +1 · 2 citations
Chemistry · Engineering · Materials Science · #Advanced Battery Materials and Technologies #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Inorganic Chemistry and Materials #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2511.16569

openalex publication_date 2025/11/20 · openalex created_date 2025/11/23 · openalex updated_date 2026/07/28

Abstract

With the growing availability of machine-learned interatomic potential (MLIP) models for materials simulations, there is an increasing demand for robust, automated, and chemically insightful benchmarking methodologies. In response, we here introduce LiPS-25, a curated benchmark dataset for a canonical series of solid-state electrolyte materials from the Li2S-P2S5 pseudo-binary compositional line, including crystalline and amorphous configurations. Together with the dataset, we present a suite of performance tests that range from conventional numerical error metrics to physically motivated evaluation tasks. With a focus on graph-based MLIP architectures, we run numerical experiments that assess (i) the effect of hyperparameters and (ii) the fine-tuning behavior of selected pre-trained ("foundational") MLIP models. Beyond the Li-P-S solid-state electrolytes, we expect that such benchmarks and their code implementations can be readily adapted to other material systems.

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• Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
• Comparing the latent features of universal machine-learning interatomic potentials

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