Kohn-Sham Density Functional Theory Electronic Structure Calculations with Linearly Scaling Computational Time and...

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Materials Science

Background:

• Hybrid Kohn-Sham density functional theory (KS-DFT) is crucial for accurate electronic structure calculations.
• Scaling limitations in traditional KS-DFT methods hinder the study of large molecular systems.
• Efficient computational methods are needed to overcome these scaling challenges.

Purpose of the Study:

• To develop and demonstrate a complete linear scaling method for hybrid KS-DFT electronic structure calculations.
• To enable accurate and efficient calculations for very large molecular systems.
• To optimize computational time and memory usage in electronic structure modeling.

Main Methods:

• Implementation of a linear scaling approach for hybrid KS-DFT.
• Direct computation of the Kohn-Sham exchange-correlation matrix in sparse form.
• Utilizing sparse data structures for efficient computational time and memory management.
• Selection of threshold values based on impact on the occupied subspace for balanced accuracy.

Main Results:

• Demonstration of linear scaling performance for hybrid KS-DFT.
• Successful application to polypeptide helix molecules with over 53,000 atoms.
• Largest calculation involved over 307,000 Gaussian basis functions on a single computer.
• Benchmarks for 3D water clusters and results using the 6-31G** basis set are also presented.

Conclusions:

• The developed linear scaling method provides a significant advancement for hybrid KS-DFT calculations.
• The method efficiently handles large systems, reducing computational cost.
• This approach opens possibilities for studying larger and more complex molecular systems with high accuracy.