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Fast periodic Gaussian density fitting by range separation.
Hong-Zhou Ye1, Timothy C Berkelbach1
1Department of Chemistry, Columbia University, New York, New York 10027, USA.
We developed range-separated Gaussian density fitting (RSGDF) for efficient periodic calculations. This method significantly speeds up computations for solids while maintaining high accuracy.
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Area of Science:
- Computational chemistry
- Materials science
- Quantum mechanics
Background:
- Periodic Gaussian density fitting (GDF) is crucial for accurate electronic structure calculations.
- Existing GDF methods can be computationally intensive, especially for large systems or dense k-point meshes.
Purpose of the Study:
- To introduce an efficient and accurate implementation of periodic Gaussian density fitting.
- To improve the computational scaling of GDF for periodic systems.
Main Methods:
- Range-separation of the Coulomb kernel into short-range (real space) and long-range (reciprocal space) components.
- Implementation of algorithmic optimizations for evaluating three-center integrals.
- Development of range-separated Gaussian density fitting (RSGDF).
Main Results:
- RSGDF demonstrates sublinear to linear scaling with the number of k-points for common mesh sizes.
- Achieved approximately ten-fold speedups compared to previous GDF methods for 3D solids.
- Introduced minimal precision loss, with errors around 10^-5 E_h in Hartree-Fock energy.
Conclusions:
- RSGDF offers a significant computational advantage for periodic electronic structure calculations.
- The method provides a balance between computational efficiency and accuracy.
- Precision can be systematically improved by adjusting auxiliary basis set size with minimal overhead.