First-Principles-Informed Machine Learning Study of Defects on the Lithium Metal Surface

Dendrite formation at the lithium anode-electrolyte interface causes degradation in lithium metal batteries. One factor that influences these branched Li-based growths is the surface and interfacial energetics associated with the presence of defects. These interfaces are disordered on the order of microns and so it is critical to understand the relationship between the microscale energetics and mesoscale. Here, we introduce machine learning (ML) as a way to connect these two scales. As a first step, we develop ML models that can predict the surface energies associated with the introduction of defects into the surface. With a dataset of defect-induced energetics calculated from density functional theory (DFT), we develop various models with conventional ML algorithms and descriptors, and neural networks with defect density features. We evaluate the performance of these models for the data set as well as for individual defect types and densities.