Accelerating materials discovery for solid state electrolytes

The development of new solid-state electrolytes is a key step in improving the performance and safety of battery technology. Although the use of first-principle methods has proved invaluable in better understanding the process at play in these materials, these methods remains extremely costly and limit the ability to model the diffusion phenomena as this one is often happening over large time-scales. To solve this issue and unlock larger time-scale and supercells, the use of force-fields has proven to be a effective solution. In particular, polarizable force-fields have been shown to be effective at reproducing accurate diffusion results. To this effect, a methodology is proposed here for the training of such polarizable force-fields using a Self-Adaptive Differential Evolution algorithm. The constant optimization of the shell positions is avoided by using its optimal position with respect to the error on cores. Furthermore, the generation of synthetic training sets is proposed through the use of Monte-Carlo dynamics and random thermal displacements. The potential of force-field modeling is then demonstrated by investigating the effect of tungsten doping on garnet type electrolytes. This investigation shows the importance of averaging over dopant distributions and highlights the complex interplay between the various effects resulting of the insertion of doping species. These various effects are isolated through the use of two distinct doping models, an implicit model where the extra positive charge is introduced as a background charge and an explicit one where the dopant is explicitly introduced. Finally the computation of the electrochemical stability of solid-state electrolytes is introduced. The different methods used to compute it are discussed and their results for relevant Li- and Na-based solid-state electrolytes are compared.