Pseudospectral implementations of long-range corrected density functional theory.

Area of Science:

• Quantum Chemistry
• Computational Physics
• Materials Science

Background:

• Density-functional theory (DFT) is a powerful tool for electronic structure calculations.
• Long-range corrected (LRC) functionals improve DFT accuracy for charge-transfer excitations.
• Pseudospectral (PS) methods offer potential computational speedups over conventional spectral (CS) methods.

Purpose of the Study:

• Implement and validate pseudospectral long-range corrected DFT (PS-LRC DFT) within the Jaguar quantum mechanics package.
• Assess the accuracy and computational efficiency of PS-LRC DFT for a wide range of molecular properties.
• Benchmark the performance of PS-LRC DFT against the conventional spectral (CS) method, particularly for large systems.

Main Methods:

• Implementation of PS-LRC DFT in the Jaguar quantum mechanics package.
• Geometry optimizations, dimmer interaction energies, polarizabilities, hyperpolarizabilities, vibrational frequencies, excitation energies (S1, T1), singlet-triplet gaps, charge transfer numbers, oscillator strengths, reaction barrier heights, electron-transfer couplings, and charge-transfer excitation energies calculations.
• Accuracy and timing benchmark analyses comparing PS-LRC DFT with the CS method.

Main Results:

• PS-LRC DFT demonstrates high accuracy with negligible deviations compared to the CS method for various molecular properties.
• Significant speedups were achieved using PS-LRC DFT: 1.4-8.4x in SCF, 22-32x in Tamm-Dancoff approximation, and 6-15x in total wall clock time for fullerene calculations.
• An average error of 0.004 eV in excitation energies was observed for PS-LRC DFT compared to the CS method.

Conclusions:

• The implemented PS-LRC DFT method is accurate and computationally efficient for a broad spectrum of quantum mechanical calculations.
• PS-LRC DFT offers substantial performance improvements, making it suitable for studying large molecular systems.
• This advancement in computational methodology enables more extensive and accurate investigations in quantum chemistry and materials science.