Beaucage, P.
, Chavez Esparza, T.
, Hexemer, A.
, Martin, T.
, Müller-Buschbaum, P.
, Roth, S.
and Wang, X.
(2025),
Event Report for "MLXN25: Machine Learning for X-ray and Neutron Scattering", Synchrotron Radiation News
(Accessed July 16, 2025)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Event Report for "MLXN25: Machine Learning for X-ray and Neutron Scattering"
Published
Author(s)
, Tanny Andrea Chavez Esparza, Alexander Hexemer, , Peter Müller-Buschbaum, Stephan Roth, Xiaoping Wang
Abstract
The MLXN25 virtual event was held on April 15, 2025, as a continuous 24-hour global event, uniting over 300 registered participants from 18 countries and 20 user facilities to discuss how machine learning (ML) is transforming X-ray and neutron science. This year's program offered a sweeping view of emerging ML methodologies across data processing, simulation, autonomous control, and instrumentation development. The event featured 31 talks, 5 tutorials, 6 open discussions, and several live demonstrations. With contributions from academia, government laboratories, and industry, MLXN25 exemplified a vibrant, global research community pushing the boundaries of scientific discovery through artificial intelligenceCitation
Synchrotron Radiation News
Pub Type
Journals
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact [email protected].
Created July 3, 2025