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Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models.

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We accelerated quantum mechanical calculations for large biomolecular systems using graphical processing units (GPUs) to speed up conductor-like polarization model (C-PCM) solvation. This GPU implementation and improved linear solvers make solvation calculations routine.

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Area of Science:

  • Computational chemistry
  • Biomolecular simulations
  • Quantum mechanics

Background:

  • The conductor-like polarization model (C-PCM) is a standard implicit solvation model.
  • Computational cost limits C-PCM application in large biomolecular systems.
  • Previous work accelerated gas-phase electronic structure calculations using GPUs.

Purpose of the Study:

  • To accelerate quantum mechanical calculations including C-PCM solvation for large biomolecular systems.
  • To extend GPU acceleration to C-PCM solvation calculations.
  • To improve the efficiency of solving linear equations within C-PCM.

Main Methods:

  • Implemented C-PCM solvation calculations on GPUs.
  • Developed strategies for accelerating linear equation solvers: dynamic convergence threshold and randomized block-Jacobi preconditioner.
  • Benchmarked performance on over 20 small proteins.

Main Results:

  • GPU implementation accelerated C-PCM integral generation over 10x compared to CPU.
  • Linear solver improvements provided an additional 3x speedup.
  • Overall C-PCM solvation calculations increased computational effort by 20-40% compared to gas phase.
  • Relative cost of C-PCM solvation decreases with larger basis sets and cavity radii.

Conclusions:

  • GPU acceleration and improved linear solvers make C-PCM solvation calculations computationally feasible for routine use.
  • The method is applicable to studying complex systems like amyloid fibrils.
  • Further investigation into conformational landscapes of biomolecular systems is enabled.