Investigating Ionic Diffusivity in Amorphous Solid Electrolytes using Machine Learned Interatomic Potentials
Aqshat Seth, Rutvij Pankaj Kulkarni, Gopalakrishnan Sai Gautam

TL;DR
This study develops and validates a machine learned interatomic potential for amorphous LiPON, enabling detailed simulations of Li$^+$ transport with high accuracy and efficiency, including across interfaces.
Contribution
We trained a neural equivariant interatomic potential for LiPON, achieving high accuracy and enabling large-scale, long-timescale simulations of Li$^+$ diffusion in amorphous electrolytes.
Findings
Li$^+$ diffusivity in bulk LiPON agrees with literature
Li-transport across Li||LiPON interface is slower than in bulk
MLIPs enable efficient modeling of complex amorphous materials
Abstract
Investigating Li transport within the amorphous lithium phosphorous oxynitride (LiPON) framework, especially across a Li||LiPON interface, has proven challenging due to its amorphous nature and varying stoichiometry, necessitating large supercells and long timescales for computational models. Notably, machine learned interatomic potentials (MLIPs) can combine the computational speed of classical force fields with the accuracy of density functional theory (DFT), making them the ideal tool for modelling such amorphous materials. Thus, in this work, we train and validate the neural equivariant Interatomic potential (NequIP) framework on a comprehensive DFT-based dataset consisting of 13,454 chemically relevant structures to describe LiPON. With an optimized training (validation) energy and force mean absolute errors of 5.5 (6.1) meV/atom and 13.6 (13.2) meV/{\AA}, respectively, we…
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Taxonomy
TopicsMachine Learning in Materials Science · Zeolite Catalysis and Synthesis · Solid-state spectroscopy and crystallography
