Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics
arxiv(2024)
摘要
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; Na_3SbS_4, a Na^+ vacancy conductor; and
Li_10GeP_2S_12, 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 open a promising path toward the rapid design of novel SSICs.
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