Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Li</mml:mi> <mml:mn>6</mml:mn> </mml:msub> <mml:msub> <mml:mi>PS</mml:mi> <mml:mn>5</mml:mn> </mml:msub> <mml:mi>Cl</mml:mi> </mml:mrow> </mml:math> using machine-learning interatomic potentials
Yongliang Ou, Yuji Ikeda, Lena Scholz, Sergiy V. Divinski, Felix Fritzen, Blazej Grabowski
Abstract
Harnessing the potential of solid-state electrolytes is crucial for advancing all-solid-state lithium-ion batteries, yet the role of grain boundaries (GBs) in ionic conductivity remains poorly understood. The authors present an active learning approach to develop machine-learning interatomic potentials with high accuracy, enabling large-scale, long-term simulations of complex GBs in polycrystalline Li${}_{6}$PS${}_{5}$Cl. Intercage diffusion of Li ions near GBs, triggered by the ``cage-opening effect'' of GBs, is notably observed. These findings offer key insights into optimizing ionic transport in solid electrolytes, paving the way for improved performance in next-generation battery technologies.