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Double Hybrids and Noncovalent Interactions: How Far Can We Go?
Eric Brémond1, Hanwei Li2, Juan Carlos Sancho-García3
1Université de Paris, ITODYS, CNRS, F-75006 Paris, France.
This study validates the PBE-QIDH/DH-SVPD protocol for accurately calculating weak noncovalent interactions in large molecular systems. The protocol shows high accuracy, especially for systems with over 1000 atoms, offering a significant speed-up.
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Molecular Interactions
Background:
- Accurate evaluation of weak noncovalent interactions in large molecular systems (up to 1000 atoms) is computationally challenging.
- Approximations are often necessary to make these calculations feasible.
Purpose of the Study:
- To assess the PBE-QIDH/DH-SVPD protocol's accuracy and robustness compared to other Density Functional Approximations.
- To investigate the impact of computational parameters on the protocol's performance for large systems.
Main Methods:
- The PBE-QIDH/DH-SVPD protocol, combining a nonempirical double hybrid functional with a small basis set, was employed.
- Three datasets (S66, L7, CiM13) with increasing molecular sizes were used for evaluation.
- The performance was analyzed concerning computational parameters and the use of the DLPNO approximation.
Main Results:
- The PBE-QIDH/DH-SVPD protocol demonstrates high accuracy for large molecular systems, including those in the CiM13 set (over 1000 atoms).
- The DLPNO approximation significantly accelerates the calculation of the perturbative correlation term for large systems.
- The study highlights limitations related to assessing reference data quality and sample size for very large molecular complexes.
Conclusions:
- The PBE-QIDH/DH-SVPD protocol is a reliable and accurate method for studying weak noncovalent interactions in large molecular systems.
- Computational efficiency can be enhanced using approximations like DLPNO for large-scale calculations.
- Further validation is needed, considering the challenges in assessing reference data quality for extremely large systems.
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