Data-driven discovery and intelligent design of artificial hybrid interphase layer for stabilizing lithium-metal anode

Progress and potentialLithium metal is considered the most promising anode material in high-energy-density batteries. However, it still faces obstacles that impede widespread commercialization due to intractable issues including growth of Li dendrites and repeated side reactions during cycling. An inorganic-organic hybrid interphase layer strategy by a self-assembled monolayer method is proposed to enable a Li-metal anode interface with good mechanical stability and excellent ionic conductivity that promotes Li uniform deposition and suppression of Li dendrite growth. A high-throughput framework, by coupling a first-principles quantum mechanical approach with machine-learning methods, was applied to identify promising F-containing organic molecules for constructing artificial inorganic-organic hybrid interphase layers. The new approach is expected to be insightful for the field of Li-metal batteries and also for a wider community involved with the computational discovery of molecules and materials beyond.Highlights•Inorganic-organic hybrid interphase layer strategy for protecting Li-metal anode•Predicting Li diffusion barrier by the identical electronic descriptors of molecules•High-throughput data-driven workflow accelerating design of self-assembled moleculesSummaryLithium metal is a promising anode material for high-energy-density batteries, but its application is hindered by safety concerns arising from dendrite growth. In this work, we propose a high-throughput workflow that combines quantum-mechanical simulations with machine learning to accurately predict self-assembled monolayers (SAMs) that can assemble an artificial inorganic-organic hybrid interphase layer on the Li-metal anode to enhance cycling stability and mitigate dendrite growth. The workflow comprises automatic data collection, first-principles simulations, and screening of candidate molecules using machine learning. We screened out 128 molecules from the PubChem database and identified the eight best candidates with low Li diffusion barriers and high mechanical stability. A structure-property relationship was established between the Li diffusion barrier and the structural characteristics of head, middle, and tail groups in the SAMs using simple quantum mechanical (QM) dipole and electrostatic potential descriptors. These results open new avenues for designing highly stable Li-metal anodes for practical use in Li-metal batteries.Graphical abstract