arXiv · 2511.16569
Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
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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Natascia L. Fragapane, Volker L. Deringer. 2025-11-20. Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials. https://arxiv.org/abs/2511.16569
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