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Long-Range Corrected Hybrid Density Functionals with Improved Dispersion Corrections.

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|November 22, 2015
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Summary
This summary is machine-generated.

Two new long-range corrected hybrid density functionals, ωM06-D3 and ωB97X-D3, were developed using DFT-D3 dispersion corrections. These functionals demonstrate high accuracy across diverse chemical applications, including thermochemistry and noncovalent interactions.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Theoretical Chemistry

Background:

  • Density Functional Theory (DFT) is a cornerstone of modern computational chemistry.
  • Accurate modeling of dispersion forces is crucial for describing noncovalent interactions.
  • Existing long-range corrected (LC) hybrid functionals may have limitations in certain applications.

Purpose of the Study:

  • To develop and validate new LC hybrid density functionals incorporating improved dispersion corrections.
  • To assess the performance of the proposed functionals across a broad spectrum of chemical problems.
  • To compare the new functionals against established methods.

Main Methods:

  • Incorporation of empirical atom-atom dispersion corrections from DFT-D3.
  • Development of two novel LC hybrid functionals: ωM06-D3 and ωB97X-D3.
  • Rigorous benchmarking against various chemical benchmarks, including thermochemistry, kinetics, and noncovalent interactions.

Main Results:

  • The proposed ωM06-D3 and ωB97X-D3 functionals exhibit high accuracy for a wide range of applications.
  • ωB97X-D3 shows improved performance for nonbonded interactions compared to ωB97X-D.
  • ωM06-D3 demonstrates superior performance for general applications, outperforming common global and LC hybrid functionals.

Conclusions:

  • The integration of DFT-D3 dispersion corrections significantly enhances the accuracy of LC hybrid functionals.
  • ωM06-D3 and ωB97X-D3 represent valuable additions to the computational chemist's toolkit for accurate predictions.
  • These functionals offer improved reliability for diverse chemical modeling tasks.