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Bond breaking with auxiliary-field quantum Monte Carlo
W A Al-Saidi1, Shiwei Zhang, Henry Krakauer
1Department of Physics, College of William and Mary, Williamsburg, VA 23187-8795, USA. al-saidi@cornell.edu
The Journal of Chemical Physics
|October 16, 2007
Summary
The phaseless auxiliary-field quantum Monte Carlo (AF QMC) method accurately maps molecular potential-energy curves during bond stretching. This advanced computational method offers improved accuracy over traditional techniques for studying electron correlation in molecules.
Area of Science:
- Computational Chemistry
- Quantum Many-Body Physics
- Materials Science
Background:
- Bond stretching in molecules is a critical benchmark for computational chemistry methods.
- Accurately describing electron correlation is essential for predicting molecular behavior.
- Approximate many-body methods require rigorous testing for reliability.
Purpose of the Study:
- To evaluate the performance of the phaseless auxiliary-field quantum Monte Carlo (AF QMC) method for bond stretching.
- To compare AF QMC with established methods like coupled cluster CCSD(T).
- To investigate the impact of different trial wave functions on AF QMC accuracy.
Main Methods:
- Utilized the phaseless auxiliary-field quantum Monte Carlo (AF QMC) method.
- Applied the method to bond stretching in BH, N(2), and H(50) systems.
- Employed unrestricted Hartree-Fock and multiconfiguration self-consistent-field wave functions as trial states.
Main Results:
- Phaseless AF QMC demonstrated superior accuracy and uniformity compared to CCSD(T) for potential-energy curves.
- The method effectively controlled the sign/phase problem in quantum Monte Carlo simulations.
- Using multideterminant trial wave functions yielded excellent results across the entire dissociation spectrum.
Conclusions:
- The phaseless AF QMC method is a robust and accurate tool for studying molecular bond stretching.
- It provides a reliable approach for investigating electron correlation effects.
- The choice of trial wave function significantly influences the accuracy and computational cost.
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