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Published on: April 8, 2020
Accurate potential energy surfaces with a DFT+U(R) approach
Heather J Kulik1, Nicola Marzari
1Department of Chemistry, Stanford University, Stanford, California 94305, USA. hkulik@stanford.edu
We present a new DFT+U method that accounts for geometric changes, improving accuracy for localized electrons in molecules. This position-dependent approach significantly enhances predictions compared to standard DFT+U.
Area of Science:
- Computational Chemistry
- Materials Science
- Quantum Mechanics
Background:
- Density Functional Theory (DFT) with Hubbard U (DFT+U) corrects self-interaction errors for localized electrons.
- Standard DFT+U often uses an averaged Hubbard U, limiting accuracy across different molecular geometries.
- Accurate modeling of localized d and f electrons is crucial for many materials and molecules.
Purpose of the Study:
- To introduce a position-dependent DFT+U (DFT+U(R)) method that incorporates geometry variations.
- To overcome the limitation of using an averaged Hubbard U in energy comparisons.
- To improve the accuracy of DFT+U for systems with significant U variations with geometry.
Main Methods:
- Developed a DFT+U approach where the Hubbard U value is self-consistently calculated and varies with geometry (DFT+U(R)).
- Applied the linear-response method to determine the position-dependent U(R).
- Validated DFT+U(R) against accurate quantum chemistry methods and experimental data for diatomic molecules.
Main Results:
- DFT+U(R) demonstrated a two- to four-fold improvement over standard DFT+U for diatomic molecules.
- Reduced errors in binding energies, vibrational frequencies, and equilibrium bond lengths.
- Showed the method's relevance for systems like transition-metal oxides where U varies significantly with geometry.
Conclusions:
- The position-dependent DFT+U(R) offers a significant advancement over standard DFT+U for localized electrons.
- Provides a criterion to assess the necessity of standard DFT+U versus DFT+U(R).
- The DFT+U(R) approach is applicable to complex systems and can be integrated into standard computational workflows.
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