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

Session

Machine Learning for XAS Prediction and Analysis

5 Jan 2026, 13:30

Siemens Auditorium (ETH Zurich)

32. Machine Learning for X-ray Spectroscopy: Hero or Zero?

Prof. Thomas Penfold (University of Newcastle)

05/01/2026, 13:30

Oral Presentation

X-ray spectroscopy (XS) is undergoing such a transformation, powered by next-generation, high-brilliance light sources. As experimental capability expands, a new challenge emerges: How can we efficiently and accurately analyse the resulting data so that the rich quantitative information encoded in each spectrum is fully exploited? Extracting such insight increasingly requires sophisticated...

18. Fine-tuning ab-initio XANES spectra calculations using the Bayesian Optimization algorithm.

Andrey Sapronov (IKFT KIT)

05/01/2026, 14:10

Oral Presentation

First-principle calculations of near-edge absorption spectra are widely used for experimental data analysis. Finding simulation parameters for the best match with experimental data may often be a challenge due to insufficient computational resources, ambiguous interpretation of spectral similarity criteria, or lack of expertise. When considering the simulation codes, such as FEFF or FDMNES as...

33. Predicting noise-free XAS spectra from noisy measurements

Dr Tomas Aidukas (PSI - Paul Scherrer Institut)

05/01/2026, 15:20

Oral Presentation