Nonequilibrium molecular dynamics of ion conduction with equivariant neural network models

The authors propose a machine-learning approach that integrates nonequilibrium molecular dynamics under a constant electric field with equivariant neural network models to evaluate ionic conductivity in solid electrolytes. In this approach, Born effective charges are predicted by an equivariant graph neural network and used to describe field-induced forces and current density. These forces are combined with unperturbed forces from an equivariant neural network potential. Applied to the representative solid electrolyte Li₁₀GeP₂S₁₂, the method achieves first-principles accuracy at a fraction of the computational cost. It also captures charge fluctuations in complex materials, providing new physical insights into ionic dynamics.