Tracking the lithiation state of LixSi from machine-learned XPS binding energies

X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon based anodes represent a paradigmatic example in Li batteries, as (de)lithiation proceeds through the formation of strongly disordered LixSi phases and crystal-amorphous transformations that are hard to characterize. Here, we introduce a computational framework that combines machine-learning (ML) prediction of core-level binding energies to large-scale atomistic simulations - Grand Canonical Monte Carlo (GCMC) complemented with molecular dynamics (MD), driven by a ML potential - for a systematic sampling of lithiation states and local atomic environments. This approach yields stoichiometry maps that capture the main spectroscopic trends observed in XPS measurements, including the distinctive Si 2p signatures associated with the crystal-to-amorphous disordering during early delithiation. This repository contains the datasets supporting this work, including the training and validation datasets used to develop the ML model and the atomistic structures generated using melt–quench–annealing (MQA) and GCMC simulations.