Machine Learning Assisted Design of Oxygen-containing Inorganic Coating Materials on Separator for Lithium Metal Anode

Abstract

The growth of lithium dendrites and its associated challenges pose significant obstacles to the widespread adoption of lithium metal. Although numerous inorganic materials offer the potential for stabilizing lithium metal anodes, the trial-error experiments are time-consuming and cost-intensive. In this work, firstly, a high-throughput screening workflow embedded with machine learning and calculation has been used to identify possible materials, which incorporates several key indicators encompassing electronic conductivity, phase stability, mechanical properties, chemical stability, and lithium-ion transport performance. Four materials were used for experiment, and both characterization and electrochemical test results show that HfO2@PP exhibits the best performance, which has the highest Young's modulus, and the Li||Li symmetry cell assembled at 1 mA cm-2, 1 mAh cm-2 can have a stable cycle of over 1000h, and the assembled Li||LFP cell has a capacity retention rate of more than 90% and an average coulombic efficiency of 99.7% after 200 cycles at 1C. This work provides a design method and ideas for Inorganic coating materials on a separator for lithium metal anode.