Bibliographic Details
| Title: |
Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments |
| Authors: |
Junqi Yin, Viktor Reshniak, Siyan Liu, Guannan Zhang, Xiaoping Wang, Zhongcan Xiao, Zachary Morgan, Sylwia Pawledzio, Thomas Proffen, Christina Hoffmann, Huibo Cao, Bryan C. Chakoumakos, Yaohua Liu |
| Source: |
Structural Dynamics, Vol 11, Iss 6, Pp 064303-064303-10 (2024) |
| Publisher Information: |
AIP Publishing LLC and ACA, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Crystallography |
| Subject Terms: |
Crystallography, QD901-999 |
| More Details: |
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2329-7778 |
| Relation: |
https://doaj.org/toc/2329-7778 |
| DOI: |
10.1063/4.0000279 |
| Access URL: |
https://doaj.org/article/ad04317a1b7c4953a39f8d752116f2f0 |
| Accession Number: |
edsdoj.04317a1b7c4953a39f8d752116f2f0 |
| Database: |
Directory of Open Access Journals |
