Ab initio and machine learning studies of solid electrolyte Li3InCl6
Disorder and high entropy effects
R. Villar Parajo (TU Delft - Applied Sciences)
Marnix Wagemaker – Mentor (TU Delft - RST/Storage of Electrochemical Energy)
Alexandros Vasileiadis – Mentor (TU Delft - RST/Storage of Electrochemical Energy)
Anastasia K. Lavrinenko – Mentor (TU Delft - RST/Storage of Electrochemical Energy)
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Abstract
As is already known, large- and medium-scale energy storage solutions are required for the much needed energy transition. Batteries become their most relevant in this circumstance, and specifically solid state batteries may have the key to solve the biggest drawbacks of current battery technologies. At their best, they offer safety and lightness while increasing storage capacity. Nonetheless, there are still hurdles to overcome, namely the complex ion kinetics in solids. Herein we present a computational study of the diffusion properties of Li3InCl6, a member of the emerging family of halide-based solid electrolytes. We also investigate two prominent approaches to enhancing ionic conductivity: cation-site disorder and high entropy. Additionally, we embark on the machine learning venture for molecular dynamics with the implementation of machine learning trained potentials in our simulations.