Issue 19, 2024

Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Abstract

Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a rapid screening among the plethora of candidate SSIC compounds have been essential in this quest. While first-principles methods are routinely exploited in this context to provide atomic-level details on ion migration mechanisms, dynamic calculations of this type are computationally expensive and limit us in the time- and length-scales accessible during the simulations. Here, we explore the potential of recently developed machine-learning force fields for predicting different ion migration mechanisms in SSICs. Specifically, we systematically investigate three classes of SSICs that all exhibit complex ion dynamics including vibrational anharmonicities: AgI, a strongly disordered Ag+-conductor; Na3SbS4, a Na+ vacancy conductor; and Li10GeP2S12, which features concerted Li+ migration. Through systematic comparison with ab initio molecular dynamics data, we demonstrate that machine-learning molecular dynamics provides very accurate predictions of the structural and vibrational properties including the complex anharmonic dynamics in these SSICs. The ab initio accuracy of machine-learning molecular dynamics simulations at relatively low computational cost opens a promising path toward the rapid design of novel SSICs.

Graphical abstract: Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

Supplementary files

Article information

Article type
Paper
Submitted
19 jan 2024
Accepted
08 abr 2024
First published
09 abr 2024
This article is Open Access
Creative Commons BY license

J. Mater. Chem. A, 2024,12, 11344-11361

Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics

T. Miyagawa, N. Krishnan, M. Grumet, C. R. Baecker, W. Kaiser and D. A. Egger, J. Mater. Chem. A, 2024, 12, 11344 DOI: 10.1039/D4TA00452C

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