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Coupled cluster theory on modern heterogeneous supercomputers.
Corzo, Hector H; Hillers-Bendtsen, Andreas Erbs; Barnes, Ashleigh; Zamani, Abdulrahman Y; Pawlowski, Filip; Olsen, Jeppe; Jørgensen, Poul; Mikkelsen, Kurt V; Bykov, Dmytro.
Afiliación
  • Corzo HH; Oak Ridge National Laboratory, Oak Ridge, TN, United States.
  • Hillers-Bendtsen AE; Department of Chemistry, University of Copenhagen, Copenhagen, Denmark.
  • Barnes A; Oak Ridge National Laboratory, Oak Ridge, TN, United States.
  • Zamani AY; Department of Chemistry and Biochemistry and Center for Chemical Computation and Theory, University of California, Merced, CA, United States.
  • Pawlowski F; Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, United States.
  • Olsen J; Department of Chemistry, Aarhus University, Aarhus, Denmark.
  • Jørgensen P; Department of Chemistry, Aarhus University, Aarhus, Denmark.
  • Mikkelsen KV; Department of Chemistry, University of Copenhagen, Copenhagen, Denmark.
  • Bykov D; Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Front Chem ; 11: 1154526, 2023.
Article en En | MEDLINE | ID: mdl-37388945
ABSTRACT
This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory-a linear-scaling, massively parallel framework-as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.
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