Limitations of Global Hybrids in Predicting the Geometries and Torsional Energy Barriers of Dimeric Systems and the Role of Hartree Fock and DFT Exchange.
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2019
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Abstract
Prediction of accurate geometries is a prerequisite for accurate prediction of molecular properties. Impact of Hartree Fock (HF) exchange (a ) on geometry in the framework of DFT is investigated by monitoring dihedral angles, bond length alternations, and torsional energy barriers of 10 dimeric systems against CCSD (ADZ/ATZ) benchmarks. A strong correlation is observed between the fraction of HF exchange, equilibrium dihedral angles, and the potential energy barriers in global hybrids. Full HF exchange is critical to accurately predict the nonplanarity. Lower fractions of (a )/larger DFT exchange (1-a ) results in overestimation of torsional energy barriers at 90 and underestimation at 0 . Large contributions of (1-a ) in global hybrid functionals tend to overestimate torsional energy barriers (90 ) and are biased toward planar geometries. However, inclusion of larger fractions of (a )/lower (1-a ) also overestimate the torsional energy barriers in syn-conformations due to the localization errors associated with HF exchange in global hybrids. Hence, irrespective of the fraction of HF/DFT exchange incorporated, global hybrids fail to accurately predict torsional energy barriers at 0 and 90 simultaneously. Long-range corrected (LC) functionals, which employ full HF exchange at longer regions, outperform global hybrid functionals in predicting geometries and torsional energy barriers of the dimeric molecules. The distance dependence of (a ) thus provides a balanced fraction of HF exchange as the dihedral torsion varies. Impact of range separation parameter on geometries is marginal in altering the planarity/nonplanarity. However, range separation parameter within 0.20-0.40 bohr predicts more reliable torsional energies and geometries. © 2019 Wiley Periodicals, Inc.
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vikramaditya2019limitationsjournal
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| Authors | Vikramaditya, Talapunur;Lin, Shiang-Tai; |
| Journal | journal of computational chemistry |
| Year | 2019 |
| DOI |
10.1002/jcc.26056
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| URL | |
| Keywords | Keywords not found |
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