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Systematic development of accurate density functionals has been a decades-long challenge for scientists.
P. Hohenberg, W. Kohn, Inhomogeneous electron gas, Physical review
1964
Earlier work this paper cites.
W. Kohn, L. J. Sham, Self-consistent equations including exchange and correlation effects, Physical review
1965
Earlier work this paper cites.
H. Stoll, C. Pavlidou, H. Preuss, Theor chim acta 49: 143;(b) stoll h, Golka E, Preuss H (1980) Theor Chim Acta
1978
Earlier work this paper cites.
A. D. Becke, Density-functional exchange-energy approximation with correct asymptotic behavior, Phys. Rev. A
1988
Earlier work this paper cites.
K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural networks
1991
Earlier work this paper cites.
J. P. Perdew, Y. Wang, Accurate and simple analytic representation of the electron-gas correlation energy, Physical review B
1992
Earlier work this paper cites.
P. M. Gill, B. G. Johnson, J. A. Pople, A standard grid for density functional calculations, Chemical Physics Letters
1993
Earlier work this paper cites.
J. P. Perdew, K. Burke, M. Ernzerhof, Generalized gradient approximation made simple, Phys. Rev. Lett
1996
Earlier work this paper cites.
A. D. Becke, Density-functional thermochemistry. v. systematic optimization of exchange-correlation functionals, The Journal of chemical physics
1997
Earlier work this paper cites.
B. Hammer, L. B. Hansen, J. K. Nørskov, Improved adsorption energetics within density-functional theory using revised perdew-burke-ernzerhof functionals, Phys. Rev. B
1999
Earlier work this paper cites.
J. P. Perdew, K. Schmidt, Jacob’s ladder of density functional approximations for the exchange-correlation energy, AIP Conference Proceedings
2001
Earlier work this paper cites.
M. Brameier, W. Banzhaf, W. Banzhaf, Linear genetic programming
2007
Earlier work this paper cites.
J.-D. Chai, M. Head-Gordon, Systematic optimization of long-range corrected hybrid density functionals, The Journal of chemical physics
2008
Earlier work this paper cites.
M. Schmidt, H. Lipson, Distilling free-form natural laws from experimental data, science
2009
Earlier work this paper cites.
J. Deng, et al
2009
Earlier work this paper cites.
D. Rappoport, F. Furche, Property-optimized gaussian basis sets for molecular response calculations, The Journal of chemical physics
2010
Earlier work this paper cites.
O. A. Vydrov, T. Van Voorhis, Nonlocal van der waals density functional: The simpler the better, The Journal of chemical physics
2010
Earlier work this paper cites.
J. K. Nørskov, F. Abild-Pedersen, F. Studt, T. Bligaard, Density functional theory in surface chemistry and catalysis, Proceedings of the National Academy of Sciences
2011
Earlier work this paper cites.
Y. Zhao, D. G. Truhlar, Applications and validations of the minnesota density functionals, Chemical Physics Letters
2011
Earlier work this paper cites.
R. Peverati, D. G. Truhlar, Improving the accuracy of hybrid meta-gga density functionals by range separation, The Journal of Physical Chemistry Letters
2011
Earlier work this paper cites.
J. C. Snyder, M. Rupp, K. Hansen, K.-R. Müller, K. Burke, Finding density functionals with machine learning, Phys. Rev. Lett
2012
Earlier work this paper cites.
J. Wellendorff, et al
2012
Earlier work this paper cites.
J. Sun, et al
2013
Earlier work this paper cites.
A. D. Becke, Perspective: Fifty years of density-functional theory in chemical physics, The Journal of chemical physics
2014
Earlier work this paper cites.
N. Mardirossian, M. Head-Gordon, ω \omega b97x-v: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy, Physical Chemistry Chemical Physics
2014
Earlier work this paper cites.
R. Peverati, D. G. Truhlar, Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
2014
Earlier work this paper cites.
R. O. Jones, Density functional theory: Its origins, rise to prominence, and future, Rev. Mod. Phys
2015
Earlier work this paper cites.
N. Mardirossian, M. Head-Gordon, Mapping the genome of meta-generalized gradient approximation density functionals: The search for b97m-v, The Journal of chemical physics
2015
Cited alongside, same era.
H. S. Yu, S. L. Li, D. G. Truhlar, Perspective: Kohn-sham density functional theory descending a staircase, The Journal of chemical physics
2016
Cited alongside, same era.
N. Mardirossian, M. Head-Gordon, ω \omega b97m-v: A combinatorially optimized, range-separated hybrid, meta-gga density functional with vv10 nonlocal correlation, The Journal of chemical physics
2016
Cited alongside, same era.
H. S. Yu, X. He, D. G. Truhlar, Mn15-l: A new local exchange-correlation functional for kohn–sham density functional theory with broad accuracy for atoms, molecules, and solids, Journal of chemical theory and computation
2016
Cited alongside, same era.
L. Li, et al
G. Galli, The long and winding road: Predicting materials properties through theory and computation, Handbook of Materials Modeling: Methods: Theory and Modeling
2020
Later among the works it cites.
L. Li, K. Burke, Recent developments in density functional approximations, Handbook of Materials Modeling: Methods: Theory and Modeling
2020
Later among the works it cites.
Y. Wang, et al
2020
Later among the works it cites.
M. Bogojeski, L. Vogt-Maranto, M. E. Tuckerman, K.-R. Müller, K. Burke, Quantum chemical accuracy from density functional approximations via machine learning, Nature communications
2020
Later among the works it cites.
M. Fujinami, R. Kageyama, J. Seino, Y. Ikabata, H. Nakai, Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential, Chemical Physics Letters
2020
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2016
Cited alongside, same era.
L. Li, T. E. Baker, S. R. White, K. Burke, Pure density functional for strong correlation and the thermodynamic limit from machine learning, Phys. Rev. B
2016
Cited alongside, same era.
K. T. Lundgaard, J. Wellendorff, J. Voss, K. W. Jacobsen, T. Bligaard, mbeef-vdw: Robust fitting of error estimation density functionals, Physical Review B
2016
Cited alongside, same era.
R. Evans, M. Oettel, R. Roth, G. Kahl, New developments in classical density functional theory, Journal of Physics: Condensed Matter
2016
Cited alongside, same era.
N. Mardirossian, M. Head-Gordon, Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals, Molecular Physics
2017
Cited alongside, same era.
F. Brockherde, et al
2017
Cited alongside, same era.
Q. Sun, et al
2017
Cited alongside, same era.
N. Mardirossian, M. Head-Gordon, Survival of the most transferable at the top of jacob’s ladder: Defining and testing the ω \omega b97m (2) double hybrid density functional, The Journal of chemical physics
2018
Cited alongside, same era.
Later among the works it cites.
R. Meyer, M. Weichselbaum, A. W. Hauser, Machine learning approaches toward orbital-free density functional theory: Simultaneous training on the kinetic energy density functional and its functional derivative, Journal of Chemical Theory and Computation
2020
Later among the works it cites.
R. A. Vargas-Hernandez, Bayesian optimization for calibrating and selecting hybrid-density functional models, The Journal of Physical Chemistry A
2020
Later among the works it cites.
R. Nagai, R. Akashi, O. Sugino, Completing density functional theory by machine learning hidden messages from molecules, npj Computational Materials
2020
Later among the works it cites.
Y. Chen, L. Zhang, H. Wang, W. E, Deepks: A comprehensive data-driven approach toward chemically accurate density functional theory, Journal of Chemical Theory and Computation
2020
Later among the works it cites.
S. Dick, M. Fernandez-Serra, Machine learning accurate exchange and correlation functionals of the electronic density, Nature communications
2020
Later among the works it cites.
S.-M. Udrescu, M. Tegmark, Ai feynman: A physics-inspired method for symbolic regression, Science Advances
2020
Later among the works it cites.
M. D. Cranmer, et al
2020
Later among the works it cites.
H. Vaddireddy, A. Rasheed, A. E. Staples, O. San, Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data, Physics of Fluids
2020
Later among the works it cites.
E. Real, C. Liang, D. R. So, Q. V. Le, AutoML-Zero: Evolving machine learning algorithms from scratch, 37th International Conference on Machine Learning (ICML)
2020
Later among the works it cites.
S.-C. Lin, G. Martius, M. Oettel, Analytical classical density functionals from an equation learning network, The Journal of Chemical Physics
2020
Later among the works it cites.
B. Kalita, L. Li, R. J. McCarty, K. Burke, Learning to approximate density functionals, Accounts of Chemical Research
2021
Later among the works it cites.
L. Li, et al
2021
Later among the works it cites.
M. F. Kasim, S. M. Vinko, Learning the exchange-correlation functional from nature with fully differentiable density functional theory, Phys. Rev. Lett
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Kirkpatrick, et al
2021
Later among the works it cites.
E. Kabliman, A. H. Kolody, J. Kronsteiner, M. Kommenda, G. Kronberger, Application of symbolic regression for constitutive modeling of plastic deformation, Applications in Engineering Science
2021
Later among the works it cites.
2021
Later among the works it cites.
J. D. Co-Reyes, et al
2021
Later among the works it cites.
A. Davies, et al
2021
Later among the works it cites.
R. Nagai, R. Akashi, O. Sugino, Machine-learning-based exchange correlation functional with physical asymptotic constraints, Phys. Rev. Research
2022
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