TY - JOUR
T1 - Event Report for “MLXN25
T2 - Machine Learning for X-ray and Neutron Scattering”
AU - Beaucage, Peter A.
AU - Chavez, Tanny
AU - Hexemer, Alexander
AU - Martin, Tyler B.
AU - Müller-Buschbaum, Peter
AU - Roth, Stephan V.
AU - Wang, Xiaoping
N1 - Publisher Copyright:
© This work was authored as part of the Contributor’s official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
PY - 2025
Y1 - 2025
N2 - The 2025 Machine Learning for X-ray and Neutron Scattering, MLXN25, virtual event was held on April 15, 2025, as a continuous 24-h 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 talks, tutorials, 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 intelligence (AI).
AB - The 2025 Machine Learning for X-ray and Neutron Scattering, MLXN25, virtual event was held on April 15, 2025, as a continuous 24-h 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 talks, tutorials, 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 intelligence (AI).
UR - https://www.scopus.com/pages/publications/105020762864
U2 - 10.1080/10448632.2025.2551389
DO - 10.1080/10448632.2025.2551389
M3 - Comment/debate
AN - SCOPUS:105020762864
SN - 1044-8632
VL - 36
SP - 3
EP - 8
JO - Neutron News
JF - Neutron News
IS - 2
ER -