Login

Symposium for Data-Driven Approaches in X-ray Absorption Spectroscopy (DataXAS)

5–6 Jan 2026
ETH Zurich
Europe/Zurich timezone

Session

Advanced Approaches to EXAFS Analysis

6 Jan 2026, 09:00
Siemens Auditorium (ETH Zurich)

Siemens Auditorium

ETH Zurich

Campus Hönggerberg

Presentation materials

There are no materials yet.

  1. 34. Unlocking Hidden Functionality Descriptors in Nanomaterials with Machine Learning – Driven XAFS
    Prof. Anatoly Frenkel (Stony Brook University)
    06/01/2026, 09:00
    Oral Presentation

    Understanding how nanomaterials work requires identifying their active units, pinpointing active sites, and providing data that inform theoretical models. Two major challenges are the intrinsic heterogeneity of active species and their dynamic restructuring in reactive environments. In the first part of my talk, I will introduce a descriptor-based approach that bridges structure and function...

    Go to contribution page
  2. 19. Convolutional AutoEncoder for Anomaly Detection and Chemical-State Identification of XAS Spectra in Operando Catalysis
    Athar Khodabakhsh (Helmholtz-Zentrum Berlin)
    06/01/2026, 09:40
    Oral Presentation

    Machine Learning (ML) techniques offer powerful tools for advancing scientific discoveries and are increasingly integrated into material science, particularly X-ray Absorption Spectroscopy (XAS) studies in situ. These methods enable applications ranging from real-time spectral analysis during catalytic reaction to the prediction of structural parameters [1,2]. As a wide range of spectroscopic...

    Go to contribution page
  3. 7. Noise-Aware Multimodal Deep Learning for Real-Time Prediction of EXAFS Fitting Quality
    Lei JIANG (Synchrotron SOLEIL)
    06/01/2026, 11:10
    Oral Presentation

    Hyperspectral X-ray absorption spectra (XAS) imaging implemented at the ROCK-SOLEIL beamline [1] offers a unique opportunity to combine second time-resolution and micrometer spatial resolution for monitoring electronic and local order transformations occurring during a chemical reaction. Individual spectra collected under operando conditions at each raw pixel of hyperspectral cubes have low...

    Go to contribution page
  4. 36. Machine learning algorithms for EXAFS analysis
    Dr Janis Timoshenko (Fritz Haber Institute of the Max Planck Society)
    06/01/2026, 11:35
    Oral Presentation
  5. 35. EXAFS analysis with Graph Neural Networks
    Dr Javier Heras (University of Barcelona)
    Oral Presentation
Building timetable...