Rotation Assisted Proton Conduction Mechanism in Solid Acid Compounds using Machine Learning Molecular Dynamics

Rotation-assisted diffusion has emerged as a prevalent mechanism in various superionic conductors, offering wide-ranging applications from highly efficient solid-state electrolytes to hydrogen electrooxidation catalysts. However, a comprehensive understanding of the specific contribution of anion dynamics to the superionic behavior across different compound types has remained elusive. In the superprotonic phase, solid acid proton conductors exhibit intriguing behaviors characterized by local proton hop within the O-H^...O bond and anion reorientation, both of which presumably contribute to long-range proton motion. Therefore, gaining insight into the intricate interplay between protons and anions in the superprotonic phase is essential for further advancing our understanding of the mechanisms underlying superprotonic behavior.<br/><br/>Previous experimental endeavors have successfully characterized the timescales of proton oscillation and anion rotation using techniques such as NMR. However, to fully interpret these timescales, computational studies are necessary to provide atomistic level mechanistic insight into the transport process. Existing computational studies, while shedding light on some aspects of the transport, are constrained by their limited scale, typically involving simulations with a few hundred atoms over a timescale of a few hundred picoseconds.<br/><br/>In this study, we employ machine learning molecular dynamics (MLMD) to investigate the dynamics of anion reorientation and its interaction with proton motion in solid acid proton conductors, specifically focusing on CsH₂PO₄ and CsHSO₄. Our approach leverages machine learning interatomic force fields (MLFFs) developed through uncertainty-aware active learning and equivariant neural networks. By combining the accuracy of ab-initio methods with the scale required to simulate thousands of atoms over nanosecond timescales, our MLFFs enable us to explore the correlation between anion dynamics and proton transfer with more sufficient statistics. Using this approach, we find that polyanion group reorientation can occur without contributing to long-range proton motion, a feature not observed in prior simulations.