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Distribution of Molecular Speeds01:27

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The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
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A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations.

Fuyuki Shimojo1, Shinnosuke Hattori1, Rajiv K Kalia1

  • 1Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242, USA.

The Journal of Chemical Physics
|May 17, 2014
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Summary

We developed a new divide-conquer-recombine (DCR) method for large quantum molecular dynamics (QMD) simulations. This approach enables efficient, large-scale simulations of materials and chemical reactions using density functional theory (DFT).

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Area of Science:

  • Computational Chemistry and Physics
  • Materials Science
  • Quantum Mechanics

Background:

  • Large-scale quantum molecular dynamics (QMD) simulations are computationally demanding.
  • Existing methods struggle with the spatiotemporal scales required for complex systems.
  • Density functional theory (DFT) is crucial for accurate quantum mechanical calculations of interatomic forces.

Purpose of the Study:

  • To introduce a novel divide-conquer-recombine (DCR) algorithmic paradigm for efficient large-scale QMD simulations.
  • To develop a lean divide-and-conquer (LDC) DFT algorithm to reduce computational cost.
  • To enable the study of complex phenomena like photoexcitation dynamics and chemical reactions at unprecedented scales.

Main Methods:

  • Developed the divide-conquer-recombine (DCR) framework, integrating divide-and-conquer (DC) and recombine phases.
  • Designed a lean divide-and-conquer (LDC) DFT algorithm utilizing density-adaptive boundary conditions and a hybrid real-reciprocal space approach.
  • Employed hybrid space-band decomposition for parallel implementation on supercomputers.
  • Utilized nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods for simulating photoexcitation dynamics.

Main Results:

  • Achieved high parallel efficiency (0.984 on 786,432 cores) for a large silicon carbide (SiC) system (50.3 million atoms).
  • Successfully performed LDC-DFT-based QMD simulations for hydrogen gas production studies (16,661 atoms).
  • Enabled the simulation of large-scale photoexcitation dynamics in a 6400-atom amorphous molecular solid, reaching experimental time scales.

Conclusions:

  • The DCR paradigm, particularly with the LDC-DFT algorithm, offers a scalable and efficient solution for large-scale QMD simulations.
  • This methodology significantly advances the capability to study complex materials behavior and chemical processes at the quantum level.
  • The approach opens new avenues for investigating excited-state dynamics and exciton behavior in materials.