Skip to main content

TECHNOLOGY AND CODE article

Front. High Perform. Comput.
Sec. Architecture and Systems
Volume 2 - 2024 | doi: 10.3389/fhpcp.2024.1390709
This article is part of the Research Topic Scientific Workflows at Extreme Scales View all 4 articles

A Galactic Approach to Neutron Scattering Science *

Provisionally accepted
  • Oak Ridge National Laboratory (DOE), Oak Ridge, United States

The final, formatted version of the article will be published soon.

    Neutron scattering science is leading to significant advances in our understanding of materials and will be key to solving many of the challenges that society is facing today. Improvements in scientific instruments are actually making it more difficult to analyze and interpret the results of experiments due to the vast increases in the volume and complexity of data being produced and the associated computational requirements for processing that data. New approaches to enable scientists to leverage computational resources are required, and Oak Ridge National Laboratory (ORNL) has been at the forefront of developing these technologies. We recently completed the design and initial implementation of a neutrons data interpretation platform that allows seamless access to the computational resources provided by ORNL. For the first time, we have demonstrated that this platform can be used for advanced data analysis of correlated quantum materials by utilizing the world's most powerful computer system, Frontier. In particular, we have shown the end-to-end execution of the DCA++ code to determine the dynamic magnetic spin susceptibility χ(q, ω) for a single-band Hubbard model with Coulomb repulsion U/t = 8 in units of the nearest-neighbor hopping amplitude t and an electron density of n = 0.65. The following work describes the architecture, design, and implementation of the platform and how we constructed a correlated quantum materials analysis workflow to demonstrate the viability of this system to produce scientific results.

    Keywords: neutron scattering, workflows, high-performance computing, data management, data analysis

    Received: 23 Feb 2024; Accepted: 24 Jun 2024.

    Copyright: © 2024 Watson, Maier, Yakubov and Doak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Gregory R. Watson, Oak Ridge National Laboratory (DOE), Oak Ridge, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.