Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery
Qintao Sun, Yan Xiang, Yue Liu, Liang Xu, Tianle Leng, Yifan Ye, Alessandro Fortunelli, William A. Goddard III, Tao Cheng
Abstract
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.
Group Members
Sun, Q., Xiang, Y., Liu, Y., Xu, L., Leng, T., Ye, Y., Fortunelli, A., III, W. A. G., & Cheng, T. (2022). Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery. *J. Phys. Chem. Lett.*, *13*(34), 8047-8054. https://doi.org/10.1021/acs.jpclett.2c02222