ML for Scientific Data - ORNL

Research Highlight

A scalable transformer model for real-time decision making in neutron scattering experiments

Achievement

The U.S. Department of Energy’s (DOE’s) neutron research facilities at Oak Ridge National Labora- tory (ORNL), including the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source (SNS), are a state-of-the-art neutron scattering facility that allows researchers to study the struc- ture and dynamics of materials at the atomic scale. At the SNS, neutrons are measured using the time-of-flight (TOF) technique as they move through a neutron beamline to interact with a sample. Large volumes of neutron scattering data are collected and recorded in neutron event mode. Optimal productivity of the TOF instrument is limited due to the lack of real-time data analysis tools. The large amount of data generated by the experiments can be challenging to process and analyze in real time, particularly for experiments that require rapid feedback and adjustment of experimental parameters. The regular computer/workstation cannot keep up with the experiment speed to provide real-time feedback to adjust experimental parameters, so connecting the supercomputers available to the neutron facility is necessary to achieve real-time data analysis and experiment steering. To address this challenge, we exploit the Frontier supercomputer at Oak Ridge Leadership Computing Facility (OLCF) to train a scalable temporal fusion transformer model for real-time decision making of TOF neutron scattering experimentation. In this paper, we present the results using Frontier to provide the processing power needed to rapidly process and analyze large volumes of single-crystal diffraction data collected at TOPAZ, a neutron time-of-flight Laue single-crystal diffractometer at the SNS.

Publication

Yin, Junqi, Siyan Liu, Viktor Reshniak, Xiaoping Wang, and Guannan Zhang, A scalable transformer model for real-time decision making in neutron scattering experiments, Journal of Machine Learning for Modelling and Computing, Vol 4 (1), pp. 95-107, 2023 (DOI:10.1615/JMachLearnModelComput.2023048607)

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