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Using Machine Learning to Find New Density Functionals


Dec 04, 2021
Bhupalee Kalita, Kieron Burke


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Generalizability of density functionals learned from differentiable programming on weakly correlated spin-polarized systems


Oct 28, 2021
Bhupalee Kalita, Ryan Pederson, Li Li, Kieron Burke

* Accepted as a conference paper in NeurIPS workshop on differentiable programming, 2021 

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Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics


Sep 17, 2020
Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, Kieron Burke


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By-passing the Kohn-Sham equations with machine learning


Jun 15, 2017
Felix Brockherde, Leslie Vogt, Li Li, Mark E. Tuckerman, Kieron Burke, Klaus-Robert Müller


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Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals


Jan 28, 2015
Kevin Vu, John Snyder, Li Li, Matthias Rupp, Brandon F. Chen, Tarek Khelif, Klaus-Robert Müller, Kieron Burke

* 15 pages, 20 figures 

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Understanding Machine-learned Density Functionals


May 27, 2014
Li Li, John C. Snyder, Isabelle M. Pelaschier, Jessica Huang, Uma-Naresh Niranjan, Paul Duncan, Matthias Rupp, Klaus-Robert Müller, Kieron Burke


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Orbital-free Bond Breaking via Machine Learning


Jun 07, 2013
John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke


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Finding Density Functionals with Machine Learning


Dec 22, 2011
John C. Snyder, Matthias Rupp, Katja Hansen, Klaus-Robert Müller, Kieron Burke

* 4 pages, 4 figures, 1 table. The Supplemental Material is included at the end of the manuscript (2 pages, 3 tables) 

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