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

J Phys Chem Lett

Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, 199 Ren'ai Road, Suzhou, Jiangsu 215123, P. R. China.

Published: September 2022

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 and reactive molecular dynamics (HAIR) and machine learning (ML) models, we present an artificial intelligence (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.

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Source
http://dx.doi.org/10.1021/acs.jpclett.2c02222DOI Listing

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