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Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

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Dataset, (2024)Related to: Yongliang Ou, Yuji Ikeda, Lena Scholz, Sergiy Divinski, Felix Fritzen, Blazej Grabowski (2024). Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials. arXiv: 2407.04126.
DOI: 10.18419/darus-4510

Abstract

The data in this repository support the findings presented in the article "Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials" by Ou et al. The repository contains the training sets, the fitted machine-learning interatomic potentials (MTPs), and the relaxed bulk and grain boundary structures. An automated script to perform the proposed quality-level-based active learning scheme is also provided.

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