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Monday, 07 July 2025 10:52

Now Online: XPS-ML-Predictor – Fast and Interpretable ML-Based XPS Spectra Prediction

Now Online: XPS-ML-Predictor – Fast and Interpretable ML-Based XPS Spectra Prediction

We developed an ML-based tool to support the scientific community in simulating C1s XPS spectra from organic molecular structures.

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).

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