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This review explores deploying accurate many-body quantum chemistry methods, like coupled-cluster, on high-performance computing (HPC) platforms. It addresses computational challenges for predictive chemical simulations.

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Area of Science:

  • Quantum Chemistry
  • Computational Science
  • High-Performance Computing (HPC)

Background:

  • Many-body quantum chemistry methods are crucial for accurate chemical predictions.
  • High-performance computing (HPC) offers the necessary computational power.
  • Deploying complex quantum methods on HPC presents significant challenges.

Purpose of the Study:

  • To review the deployment of many-body quantum chemistry methods on HPC platforms.
  • To focus on highly accurate, predictive methods such as coupled-cluster.
  • To discuss the computational traits and challenges of these methods in HPC contexts.

Main Methods:

  • Review of relevant scientific literature.
  • Analysis of modern and future HPC architectures.
  • Examination of canonical and reduced-scaling formulations of many-body methods.

Main Results:

  • Identified computational traits of many-body methods relevant to HPC.
  • Highlighted challenges in realizing these methods on parallel HPC systems.
  • Provided an overview of the current and future HPC landscape for quantum chemistry.

Conclusions:

  • Successful deployment of accurate many-body methods on HPC is essential for predictive chemistry.
  • Understanding computational demands and HPC architecture is key to overcoming deployment challenges.
  • Continued advancements in HPC are vital for pushing the boundaries of quantum chemical simulations.