Accelerated Discovery of Solid-State Electrolytes Using Bayesian Optimization

Current lithium batteries do not fully meet the longevity
and safety
requirements of electric vehicles. Novel solid-state lithium-ion batteries
could be a compelling solution to these problems. In this work, we
unravel some of these new materials with potentially high lithium
conductivity by using a Bayesian optimization approach. This involves
exploring the material space for new solid-state electrolyte materials
with the objective of maximizing lithium diffusivity. The materials
selected by the Bayesian optimization algorithm are then examined
using ab initio molecular dynamics to estimate their diffusion energy
barrier. We establish that the materials are electronic insulators,
a requirement in electrolyte materials, by computing the electronic
bandgaps of each of the selected materials using a hybrid exchange
method and then examine the stability of the materials at the lithium
metal anode interface by computing the crystal decomposition energies.
Out of the selected materials, we find that Li3YBr6 has a reasonably low diffusion barrier, a high bandgap, and
is potentially the most stable material at the lithium metal interface.
In addition to introducing stable and high-diffusivity solid-state
electrolyte materials, our work presents a material discovery method
that can be applied to a broad range of applications.