Tracking the lithiation state of LixSi from machine‑learned XPS binding energies
X-ray photoelectron spectroscopy (XPS) is used to probe chemical states in battery materials, but complex microstructures hinder quantitative interpretation. The study introduces a computational framework that combines machine‑learning prediction of core‑level binding energies with grand‑canonical Monte Carlo and molecular dynamics simulations to systematically sample lithiation states and local atomic environments in silicon anodes. The resulting stoichiometry maps reproduce key spectroscopic trends observed in operando and ex situ XPS, including Si 2p signatures of crystal‑to‑amorphous diso…
X-ray photoelectron spectroscopy (XPS) is used to probe chemical states in battery materials, but complex microstructures hinder quantitative interpretation. The study introduces a computational framework that combines machine‑learning prediction of core‑level binding energies with grand‑canonical Monte Carlo and molecular dynamics simulations to systematically sample lithiation states and local atomic environments in silicon anodes. The resulting stoichiometry maps reproduce key spectroscopic trends observed in operando and ex situ XPS, including Si 2p signatures of crystal‑to‑amorphous diso…
It demonstrates how Swiss AI‑driven machine‑learning methods can advance the quantitative analysis of battery materials, supporting Switzerland’s focus on AI‑enabled materials research.
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