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DE-SC0022311: Artificial intelligence and data science enabled predictive modeling of collective phenomena in strongly correlated quantum materials

Award Status: Active
  • Institution: The University of Tennessee, Knoxville, TN
  • UEI: FN2YCS2YAUW3
  • DUNS: 003387891
  • Most Recent Award Date: 09/22/2021
  • Number of Support Periods: 1
  • PM: Graf, Matthias
  • Current Budget Period: 09/01/2021 - 08/31/2022
  • Current Project Period: 09/01/2021 - 08/31/2024
  • PI: Johnston, Steven
  • Supplement Budget Period: N/A
 

Public Abstract

The collective behavior of an enormous number of electrons and atoms in strongly correlated quantum materials gives rise to a host of novel states of matter, each with the potential to revolutionize science and energy-related technologies. But to harness these novel states, we must understand the microscopic mechanisms driving and shaping their collective behavior. This task, in turn, requires solving a quantum many-body problem, a grand challenge of the scientific community. Fortunately, recent advances in artificial intelligence (AI), machine learning (ML), and data science suggest promising routes forward.

Our project combines theoretical and experimental condensed matter physicists (CMP) and experts in AI, ML, and methods to advance our predictive modeling capabilities for correlated quantum materials. We will achieve this ambitious goal through the synthesis of three key research efforts. The first focuses on developing robust, data-driven methodologies for generating and verifying many-body models for quantum materials, both from inelastic scattering experiments and more complicated high-energy model simulations. Developing these methods in a data-centric way will help elucidate the basic principles behind novel physical phenomena, thus providing a powerful guide to searches for new states of matter and the foundation for advanced modeling and engineering of materials. To this end, our second effort focuses on developing new state-of-the-art quantum Monte Carlo (QMC) methods powered by physics-aware AI and ML algorithms. These new methods will allow us to address previously inaccessible problems and utilize leadership-class computing facilities to their full potential. Finally, these efforts will meet in our third thrust, which focuses on creating end-to-end experiment and theory workflows. This effort will begin linking our AI-enhanced theoretical frameworks directly with experiments to create new workflows that can be implemented at national DOE user facilities.

Our efforts will create new research tools that can be broadly deployed to various quantum systems of interest to the DOE, accelerating discovery. Armed with these capabilities, we will study several fundamental research problems that conventional methods cannot tackle. Examples include quantum magnets with nontrivial topological properties, understanding the physics of high-temperature superconductors, and identifying principles for engineering functional properties at the interfaces of quantum materials.

The University of Tennessee, Knoxville, leads this project in collaboration with UC Davis, San Jose State University, and Brookhaven, Los Alamos, and Oak Ridge National Laboratories.

 



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