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A sparse algorithm for the evaluation of the local energy in quantum Monte Carlo
Alán Aspuru-Guzik1, Romelia Salomón-Ferrer, Brian Austin
1Kenneth S. Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California at Berkeley, Berkeley, California 94720-1460, USA. alan@aspuru.com
Journal of Computational Chemistry
|March 12, 2005
Summary
A new quantum Monte Carlo (QMC) algorithm improves computational efficiency for large molecules. This method enables linear scaling for Slater determinant evaluation, expanding QMC applicability.
Area of Science:
- Computational chemistry
- Quantum mechanics
- Materials science
Background:
- Quantum Monte Carlo (QMC) methods are powerful tools for electronic structure calculations.
- Evaluating Slater determinants is computationally intensive, limiting QMC applications to smaller systems.
- Localized orbitals can improve the efficiency of quantum chemical calculations.
Purpose of the Study:
- To develop a novel algorithm for the sparse representation and evaluation of Slater determinants in QMC.
- To extend the applicability of QMC methods to larger molecular systems.
- To analyze the scaling of computational cost with system size.
Main Methods:
- Developed a new algorithm for sparse representation of Slater determinants.
- Employed localized orbitals within a Slater-type orbital basis set.
- Applied the algorithm to systems with up to 390 electrons.
Main Results:
- The new algorithm significantly enhances the size of molecules treatable by QMC.
- The computational cost of Slater determinant evaluation exhibits linear scaling with system size.
- Demonstrated the practical feasibility of the approach on substantial electronic systems.
Conclusions:
- The presented algorithm offers a computationally efficient approach for QMC calculations.
- Linear scaling reduces the computational burden, making larger systems accessible.
- This work paves the way for more extensive QMC studies in chemistry and materials science.