Vibrational, structural, and chemical fingerprints of ion diffusion in crystalline solids
Predicting mobile-ion self-diffusivity $D^*$ from molecular dynamics (MD) simulations is essential for identifying promising solid-state electrolytes, but directly simulating ion diffusion is computationally expensive, particularly with high-accuracy machine learning interatomic potentials (MLIPs). Diffusion is a slow, emergent process that requires long trajectories to converge. Thermodynamic properties, by contrast, converge much faster: the enthalpy $h$, vibrational entropy $s_{vib}$, and 2-body, excess configurational entropy $s^{ex}_{2,config}$ can be extracted from comparatively short MD trajectories, and they encode rich information about the free energy landscape from which transport properties like self-diffusivity ultimately arise. Intuitive correlations are discussed between these thermodynamic properties and ion diffusion, motivating a data-driven approach to exploit this link. A simple neural network was trained to predict diffusivity from features computed over short MD trajectories: a vibrational fingerprint (the vibrational density of states, VDOS) and a structural fingerprint (the radial distribution function, RDF), conditioned on chemistry information encoded in the MLIP embedding. This combination allows the model to predict the converged $\log_{10} D^*$ (cm$^2$/s) \textemdash\ normally obtained from significantly longer MD simulations \textemdash\ with a mean absolute error of 0.398 and a Spearman's rank correlation $\rho$ of 0.844.