HLE17: An Improved Local Exchange–Correlation Functional for Computing Semiconductor Band Gaps and Molecular Excitation Energies

2017 ◽  
Vol 121 (13) ◽  
pp. 7144-7154 ◽  
Author(s):  
Pragya Verma ◽  
Donald G. Truhlar
Keyword(s):  
Band Gaps ◽  
Excitation Energies ◽  
Local Exchange ◽  
Exchange Correlation ◽  
Molecular Excitation

Local Exchange-Correlation Potentials and the Fermi Surface of Copper

1972 ◽  
Vol 6 (12) ◽  
pp. 4367-4370 ◽  
Author(s):  
J. F. Janak ◽  
A. R. Williams ◽  
V. L. Moruzzi
Keyword(s):  
Fermi Surface ◽  
Local Exchange ◽  
Exchange Correlation

Assessing cathode property prediction via exchange-correlation functionals with and without long-range dispersion corrections

2021 ◽  
Author(s):  
Olivia Long ◽  
Gopalakrishnan Sai Gautam ◽  
Emily Ann Carter
Keyword(s):  
Transition Metal ◽  
Metal Oxides ◽  
Lithium Ion Batteries ◽  
Long Range ◽  
Transition Metal Oxides ◽  
Lithium Ion ◽  
Band Gaps ◽  
Property Prediction ◽  
Exchange Correlation

We benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as...

scholarly journals Large-Scale Benchmark of Exchange–Correlation Functionals for the Determination of Electronic Band Gaps of Solids

2019 ◽  
Vol 15 (9) ◽  
pp. 5069-5079 ◽  
Author(s):  
Pedro Borlido ◽  
Thorsten Aull ◽  
Ahmad W. Huran ◽  
Fabien Tran ◽  
Miguel A. L. Marques ◽  
...  
Keyword(s):  
Large Scale ◽  
Band Gaps ◽  
Electronic Band ◽  
Exchange Correlation

Explicit local exchange-correlation potentials

1971 ◽  
Vol 4 (14) ◽  
pp. 2064-2083 ◽  
Author(s):  
L Hedin ◽  
B I Lundqvist
Keyword(s):  
Local Exchange ◽  
Exchange Correlation

scholarly journals Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Maituo Yu ◽  
Shuyang Yang ◽  
Chunzhi Wu ◽  
Noa Marom
Keyword(s):  
Machine Learning ◽  
Transition Metal Oxides ◽  
Density Functional ◽  
Functional Results ◽  
Bayesian Optimization ◽  
Hybrid Functional ◽  
Band Structures ◽  
Local Exchange ◽  
Functional Theory ◽  
Exchange Correlation

AbstractWithin density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+U largely depends on the chosen Hubbard U values. We propose an approach to determining the optimal U parameters for a given material by machine learning. The Bayesian optimization (BO) algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals. This approach is demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The band structures obtained using the BO U values are in agreement with hybrid functional results. Additionally, comparison to the linear response (LR) approach to determining U demonstrates that the BO method is superior.

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