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.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1021/acs.jpclett.2c02222 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!