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Small-Basis Set Density-Functional Theory Methods Corrected with Atom-Centered Potentials
Viki Kumar Prasad1, Alberto Otero-de-la-Roza2, Gino A DiLabio1
1Department of Chemistry, University of British Columbia, Okanagan, 3247 University Way, Kelowna, British Columbia V1V 1V7, Canada.
New atom-centered potentials (ACPs) enhance Density Functional Theory (DFT) methods for accurate modeling of molecular interactions and thermochemistry. These ACP-corrected DFT methods achieve high accuracy at a low computational cost, suitable for large systems.
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Theoretical Chemistry
Background:
- Density Functional Theory (DFT) is widely used for modeling noncovalent interactions and thermochemistry.
- Inaccurate DFT functionals and limited basis sets are primary error sources for chemical property predictions.
- Accurate calculations necessitate appropriate functionals and large basis sets, increasing computational cost.
Purpose of the Study:
- To introduce three new DFT methods incorporating atom-centered potentials (ACPs) for improved accuracy.
- To develop ACPs for elements H, B, C, N, O, F, Si, P, S, and Cl.
- To minimize errors in predicted chemical properties while maintaining low computational expense.
Main Methods:
- Developed three ACP-corrected DFT methods: BLYP/6-31G*-ACP, M06-2X/6-31G*-ACP, and CAM-B3LYP/6-31G*-ACP.
- ACPs are one-electron potentials designed to correct DFT/basis-set deficiencies.
- Optimized ACP parameters using an extensive dataset (118,655 points) of high-quality computational data.
Main Results:
- ACP-corrected methods demonstrated accuracy comparable to high-level wavefunction theory methods.
- Achieved accuracy close to complete basis set (CBS) limit calculations.
- Maintained computational cost similar to double-ζ DFT methods.
Conclusions:
- The new BLYP/6-31G*-ACP, M06-2X/6-31G*-ACP, and CAM-B3LYP/6-31G*-ACP methods offer a balance of accuracy and efficiency.
- These ACP-corrected DFT methods are well-suited for large molecular systems.
- Enables accurate prediction of noncovalent, thermochemical, and kinetic properties with reduced computational resources.
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