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Development of the FMO/RI-MP2 Fully Analytic Gradient Using a Hybrid-Distributed/Shared Memory Programming Model.

Buu Q Pham1, Mark S Gordon1

  • 1Department of Chemistry and Ames Laboratory , Iowa State University , Ames , Iowa 50011 , United States.

Journal of Chemical Theory and Computation
|January 4, 2020
PubMed
Summary
This summary is machine-generated.

We developed an efficient computational method for calculating molecular gradients using fragment molecular orbital (FMO) and resolution-of-the-identity (RI) approximations. This approach significantly speeds up calculations for large molecular systems while maintaining accuracy.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Theoretical Chemistry

Background:

  • Accurate calculation of molecular gradients is crucial for understanding chemical reactions and molecular properties.
  • Traditional methods become computationally expensive for large molecular systems.
  • Fragment Molecular Orbital (FMO) methods offer a way to parallelize calculations, but efficiency improvements are still needed.

Purpose of the Study:

  • To derive and implement a fully analytic gradient for second-order Møller-Plesset perturbation theory (MP2) within the FMO framework, incorporating the resolution-of-the-identity (RI) approximation.
  • To develop a hybrid multilevel parallel programming model for efficient computation.
  • To validate the accuracy and performance of the new FMO/RI-MP2 analytic gradient.

Main Methods:

  • Derivation of the analytic gradient for FMO/MP2 theory with RI approximation.
  • Implementation using a hybrid multilevel parallel programming model (GDDI and OpenMP).
  • Validation through comparison with numerical gradients and molecular dynamics simulations (NVE ensembles).

Main Results:

  • The FMO/RI-MP2 analytic gradient was successfully implemented and validated, showing high accuracy (errors ~10^-6-10^-5 Hartree/Bohr).
  • The RI approximation introduced minimal error (~10^-5 Hartree/Bohr) while providing a significant speedup of 4.0-8.0x compared to non-RI FMO/MP2.
  • Excellent node linear scaling and high parallel efficiency (>90%) were demonstrated for large water clusters (up to 2165 molecules) on 300-800 KNL nodes.

Conclusions:

  • The developed FMO/RI-MP2 analytic gradient method is accurate and highly efficient for large molecular systems.
  • The hybrid parallel programming model effectively preserves linear scaling, enabling large-scale quantum chemical calculations.
  • This method represents a significant advancement for computational chemistry, facilitating studies of complex molecular systems.