Fractional Kohn–Sham Occupancies from a Strong-Correlation Density Functional

2014 ◽  
pp. 175-186 ◽  
Author(s):  
Axel D. Becke
Keyword(s):  
Strong Correlation ◽  
Density Functional

Electronic or steric effects: Detailed DFT investigations of titanium amino based Kaminsky type olefin catalysts

2018 ◽  
Author(s):  
Jörg Saßmannshausen
Keyword(s):  
Density Functional Theory ◽  
Strong Correlation ◽  
Density Functional ◽  
Steric Effects ◽  
Aryl Group ◽  
Functional Theory

We report detailed Density Functional Theory (DFT) investigations of a series of structurally similar titanium (IV) chelating σ-aryl catalysts. Particular attention was paid to the electronic charges of the Ti, C ipso of the substituted aryl group and the benzylic CH<sub>2</sub> and C<i><sub>ipso</sub></i> atoms. The Bader and NBO derived charges were compared with the recently reported polymerisation results by Chan. We found a strong correlation between the relative energies of one of the computed isomers and the activity of the catalyst. Neither NBO nor Bader charges could be convincingly correlated to the observed activity.

scholarly journals Strong Correlation in Kohn-Sham Density Functional Theory

2012 ◽  
Vol 109 (24) ◽  
Author(s):  
Francesc Malet ◽  
Paola Gori-Giorgi
Keyword(s):  
Density Functional Theory ◽  
Strong Correlation ◽  
Density Functional ◽  
Functional Theory

Strong correlation in density functional theory: general discussion

2020 ◽  
Vol 224 ◽  
pp. 373-381
Author(s):  
Emmanuel Fromager ◽  
Nikitas Gidopoulos ◽  
Paola Gori-Giorgi ◽  
Trygve Helgaker ◽  
Pierre-François Loos ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Strong Correlation ◽  
Density Functional ◽  
Functional Theory

Simulation of All-Order Density-Functional Perturbation Theory, Using the Second Order and the Strong-Correlation Limit

2000 ◽  
Vol 84 (22) ◽  
pp. 5070-5073 ◽  
Author(s):  
Michael Seidl ◽  
John P. Perdew ◽  
Stefan Kurth
Keyword(s):  
Perturbation Theory ◽  
Strong Correlation ◽  
Density Functional ◽  
Second Order ◽  
Density Functional Perturbation Theory

Excited-state surfaces of ethylene from the B13 strong-correlation density functional

2015 ◽  
Vol 113 (13-14) ◽  
pp. 1884-1889 ◽  
Author(s):  
Axel D. Becke
Keyword(s):  
Excited State ◽  
Strong Correlation ◽  
Density Functional

scholarly journals Publisher's Note: Pure density functional for strong correlation and the thermodynamic limit from machine learning [Phys. Rev. B 94 , 245129 (2016)]

2021 ◽  
Vol 103 (11) ◽  
Author(s):  
Li Li ◽  
Thomas E. Baker ◽  
Steven R. White ◽  
Kieron Burke
Keyword(s):  
Machine Learning ◽  
Thermodynamic Limit ◽  
Strong Correlation ◽  
Density Functional

scholarly journals Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

2020 ◽  
Author(s):  
Fang Liu ◽  
Chenru Duan ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
High Throughput ◽  
Strong Correlation ◽  
High Throughput Screening ◽  
Density Functional ◽  
Chemical Space ◽  
Occupation Number ◽  
Open Shell ◽  
Homo Lumo

<p>Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3<i>d</i> transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. </p>

Recent advances in ensemble density functional theory and linear response theory for strong correlation

2021 ◽  
Author(s):  
Seunghoon Lee ◽  
Woojin Park ◽  
Hiroya Nakata ◽  
Michael Filatov ◽  
Cheol Ho Choi
Keyword(s):  
Density Functional Theory ◽  
Strong Correlation ◽  
Linear Response ◽  
Density Functional ◽  
Linear Response Theory ◽  
Response Theory ◽  
Functional Theory ◽  
Recent Advances

Fully Nonlocal Functionals Along the Adiabatic Connection of Density Functional Theory

2019 ◽  
Author(s):  
Stefan Vuckovic
Keyword(s):  
Density Functional Theory ◽  
Strong Correlation ◽  
Density Functional ◽  
Correlation Energy ◽  
Electronic Effects ◽  
Functional Theory ◽  
Strongly Interacting ◽  
Kinetic Component ◽  
Exact Exchange ◽  
New Framework

Inspired by the exact form of the strongly interacting limit of density functional theory, Vuckovic and Gori Giorgi have recently proposed [J. Phys. Chem. Lett. 2017, 8, 2799] the multiple radii functional (MRF), a new framework for the construction of exchange-correlation (xc) energy approximations able to describe strong correlation electronic effects. To facilitate the construction of improved approximations based on the MRF functional, in the present work we use reverse engineering strategies to reveal the forms of the MRF functional which reproduce the exact xc functional for small atoms. We also develop a procedure that allows the MRF functional to be built on the top of exact exchange. Using the adiabatic connection representation of the xc functional, we also investigate routes for the construction of the correlation functional by combining information from the physical, weakly and strongly interacting regimes. We highlight the advantages of this approach over previous adiabatic connection-based approaches for the treatment of strong correlation and discuss how it can be used for recovering the presently missing kinetic component of the correlation energy in the MRF framework.<br> <pre><br></pre>

scholarly journals Pure density functional for strong correlation and the thermodynamic limit from machine learning

2016 ◽  
Vol 94 (24) ◽  
Author(s):  
Li Li ◽  
Thomas E. Baker ◽  
Steven R. White ◽  
Kieron Burke
Keyword(s):  
Machine Learning ◽  
Thermodynamic Limit ◽  
Strong Correlation ◽  
Density Functional
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