Login / Signup

Massively parallel GPU enabled third-order cluster perturbation excitation energies for cost-effective large scale excitation energy calculations.

Andreas Erbs Hillers-BendtsenDmytro BykovAshleigh L BarnesDmitry LiakhHector H CorzoJeppe OlsenPoul JørgensenKurt V Mikkelsen
Published in: The Journal of chemical physics (2023)
We present here a massively parallel implementation of the recently developed CPS(D-3) excitation energy model that is based on cluster perturbation theory. The new algorithm extends the one developed in Baudin et al. [J. Chem. Phys., 150, 134110 (2019)] to leverage multiple nodes and utilize graphical processing units for the acceleration of heavy tensor contractions. Furthermore, we show that the extended algorithm scales efficiently with increasing amounts of computational resources and that the developed code enables CPS(D-3) excitation energy calculations on large molecular systems with a low time-to-solution. More specifically, calculations on systems with over 100 atoms and 1000 basis functions are possible in a few hours of wall clock time. This establishes CPS(D-3) excitation energies as a computationally efficient alternative to those obtained from the coupled-cluster singles and doubles model.
Keyphrases
  • density functional theory
  • energy transfer
  • molecular dynamics
  • molecular dynamics simulations
  • machine learning
  • primary care
  • deep learning
  • quantum dots
  • monte carlo
  • sentinel lymph node
  • lymph node
  • radiation therapy