The CNR-ISM team has launched XPS-ML-Predictor, a user-friendly web app leveraging machine learning to predict C1s X-ray photoelectron spectra of organic molecules containing C, N, O, S, and halogens (F–I). The ML model is trained on high-quality ΔSCF-PW-DFT (B3LYP) data and generates spectra directly from .xyz molecular structures (view https://tinyurl.com/27m97x4u).

The tool:

• Predicts site-specific C1s core ionization energies.
• Simulates and visualizes atom-specific XPS spectra.
• Is openly available and easy to use.

This work was supported by ICSC – National Research Center in HPC, Big Data, and Quantum Computing (Grant CN00000013, NextGenerationEU) and the Italian Ministry of University and Research (MUR) under the PRIN 2022 program (project “NIR+,” Grant 2022BREBFN).