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Accelerated Discovery of Solid-State Electrolytes Using Bayesian Optimization
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AI
DOI：10.1021/acs.jpcc.5c00954.png)
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Abstract
En 中文
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.
Keywords:
LITHIUM ION CONDUCTION
LI10GEP2S12
INSIGHTS
Journal
IF:
3.2
Papers:
5.6W
Citations:
15.0W