Machine learning for data-driven design of high-safety lithium metal anode

Here, we present a protocol for developing an inorganic -organic hybrid interphase layer using the self -assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open -sourced databases and calculating their microscopic properties. We then detail procedures for developing a machine learning model for predicting the ionic diffusion barrier and preparing the inputs for prediction. This protocol enables a cost-effective workflow to identify promising self -assembled monolayers with exceptional performance. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).1