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Revised Damping Parameters for the D3 Dispersion Correction to Density Functional Theory.
Daniel G A Smith1, Lori A Burns2, Konrad Patkowski1
1Department of Chemistry and Biochemistry, Auburn University , Auburn, Alabama 36849, United States.
Refitting Grimme's DFT-D3 damping parameters using expanded interaction energy databases improved accuracy. New training sets enhance the description of intermolecular interactions, reducing average errors for some methods.
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Materials Science
Background:
- The accuracy of density functional theory (DFT) methods, particularly for non-covalent interactions, relies heavily on empirical dispersion corrections like DFT-D3.
- Original DFT-D3 parameters were fitted using limited benchmark sets, potentially lacking representativeness for diverse interaction types and geometries.
Purpose of the Study:
- To re-evaluate and improve the accuracy of Grimme's DFT-D3 dispersion corrections.
- To assess the impact of updated and expanded benchmark datasets on the performance of DFT-D3.
- To provide a more robust description of intermolecular interactions across various chemical systems.
Main Methods:
- Augmentation of conventional benchmark sets with new databases like side chain-side chain (SSI) interactions derived from crystal data.
- Extension of existing databases (e.g., S22×5) to include shorter intermolecular distances.
- Retraining of DFT-D3 damping parameters using an expanded dataset of 1526 interaction energies.
- Validation of the refitted parameters against a new, independent dataset of 6773 interaction energies.
Main Results:
- The refitted DFT-D3 parameters show varying degrees of accuracy improvement across different DFT functionals.
- For PBE-D3, refitting led to an almost two-fold decrease in average error.
- For LC-ωPBE-D3, the accuracy remained largely unchanged, indicating robustness.
- The expanded training set provides a more balanced description of interaction distances and types.
Conclusions:
- Refitting DFT-D3 parameters with larger, more diverse datasets significantly enhances the accuracy of predicting interaction energies for certain functionals.
- The updated approach offers a more reliable description of non-covalent interactions crucial for molecular modeling and materials science.
- The choice of DFT functional critically influences the benefits gained from refitting DFT-D3 parameters.
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