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New parallel algorithm for MP2 energy gradient calculations.

Kazuya Ishimura1, Peter Pulay, Shigeru Nagase

  • 1Department of Theoretical Molecular Science, Institute for Molecular Science, Myodaiji, Okazaki, Aichi 444-8585, Japan.

Journal of Computational Chemistry
|April 24, 2007
PubMed
Summary
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A new parallel algorithm efficiently calculates analytic energy derivatives for second-order Møller-Plesset perturbation theory (MP2). This method is ideal for optimizing large molecules, especially those with significant noncovalent interactions.

Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Theoretical Chemistry

Background:

  • Accurate calculation of molecular geometries is crucial for understanding chemical properties.
  • Second-order Møller-Plesset perturbation theory (MP2) is a widely used method for electronic structure calculations.
  • Efficient computation of analytic energy derivatives is essential for geometry optimization.

Purpose of the Study:

  • To develop and implement a novel parallel algorithm for calculating analytic energy derivatives in MP2 theory.
  • To enable efficient geometry optimization for large molecules, particularly those with noncovalent interactions.

Main Methods:

  • The study presents a new parallel algorithm based on a two-step MP2 energy calculation approach.
  • The algorithm was implemented in the GAMESS quantum chemistry program.

Related Experiment Videos

  • Performance was evaluated using test calculations on taxol and luciferin molecules with various basis sets.

    • Main Results:

    • The developed parallel algorithm demonstrates high parallel efficiency.
    • Timing results for taxol and luciferin calculations, including large-scale computations with up to 80 CPU cores, are presented.
    • The efficiency was validated across different basis sets, including 6-31G, 6-31G(d), aug-cc-pVDZ, and aug-cc-pVTZ.

    Conclusions:

    • The new parallel algorithm offers a significant advancement for MP2 calculations.
    • The method is well-suited for the geometry optimization of large molecular systems.
    • The high parallel efficiency ensures scalability for demanding computational tasks.