Machine Learning Predicts the X‑ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery

X-ray photoelectron spectroscopy (XPS) is a powerful
surface analysis
technique widely applied in characterizing the solid electrolyte interphase
(SEI) of lithium metal batteries. However, experiment XPS measurements
alone fail to provide atomic structures from a deeply buried SEI,
leaving vital details missing. By combining hybrid ab initio and reactive molecular dynamics (HAIR) and machine learning (ML)
models, we present an artificial intelligence ab initio (AI-ai) framework to predict the XPS of a SEI. A localized high-concentration
electrolyte with a Li metal anode is simulated with a HAIR scheme
for ∼3 ns. Taking the local many-body tensor representation
as a descriptor, four ML models are utilized to predict the core level
shifts. Overall, extreme gradient boosting exhibits the highest accuracy
and lowest variance (with errors ≤ 0.05 eV). Such an AI-ai
model enables the XPS predictions of ten thousand frames with marginal
cost.