Mechanisms and stability of Li dynamics in amorphous Li-Ti-P-S-based mixed ionic–electronic conductors: A machine learning molecular dynamics study

Mixed ionic–electronic conductors (MIECs) exhibit both high ionic and electronic conductivity to improve the battery performance. In this work, we investigate the mechanism and stability of transport channels in our recently developed MIEC material, amorphous Ti-doped lithium phosphorus sulfide (LPS), using molecular dynamics (MD) simulations with a 99% accurate machine-learning force field (MLFF) trained on ab initio MD data. The achieved MLFF helps efficient large-scale MD simulations on LPS with three Ti concentrations (10%, 20%, and 30%) and six temperatures (25°C to 225°C) to calculate ionic conductivity, activation energy, Li-ion transport mechanism, and configurational entropy. Results show that ionic conductivities and activation energies are consistent with our recent experimental values. Moreover, Li-ion transport occurs via free-volume diffusion facilitated by the formation of disordered Li-S polyhedra. The enhanced stability of transport channels at 10% and 20% Ti doping, compared to 0% and 30%, is observed by analyzing the vibrational and configurational entropy of these disordered Li-S polyhedra. Overall, this study highlights the utility of MLFF-based large-scale MD simulations in explaining the transport mechanism and the stability of Li-ion in Ti-doped LPS electrolyte with significant computational efficiency. • A 99%-accurate machine learning force field (MLFF) enabled large-scale molecular dynamics simulations of Ti-doped LPS, achieving ab initio -level accuracy at reduced computational cost. • Revealed free-volume diffusion as the dominant Li-ion transport mechanism, mediated by disordered Li-S polyhedra in the amorphous LPS. • Identified 10%–20% Ti doping as the optimal spot for enhanced transport channel stability, while 30% doping degraded performance as validated by entropy analysis. • Predicted ionic conductivities and activation energies matched experimental data, validating simulations with real-world performance. • Demonstrated how MLFF-driven molecular dynamics can efficiently guide the development of mixed ionic–electronic conductors based high-performance solid electrolytes.