Search Results for author: Kieron Burke

Found 6 papers, 1 papers with code

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

no code implementations28 Oct 2021 Bhupalee Kalita, Ryan Pederson, Li Li, Kieron Burke

Kohn-Sham regularizer (KSR) is a machine learning approach that optimizes a physics-informed exchange-correlation functional within a differentiable Kohn-Sham density functional theory framework.

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

1 code implementation17 Sep 2020 Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, Kieron Burke

Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures.

By-passing the Kohn-Sham equations with machine learning

no code implementations9 Sep 2016 Felix Brockherde, Leslie Vogt, Li Li, Mark E. Tuckerman, Kieron Burke, Klaus-Robert Müller

Last year, at least 30, 000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, ranging from materials science to biochemistry to astrophysics.

Understanding Machine-learned Density Functionals

no code implementations4 Apr 2014 Li Li, John C. Snyder, Isabelle M. Pelaschier, Jessica Huang, Uma-Naresh Niranjan, Paul Duncan, Matthias Rupp, Klaus-Robert Müller, Kieron Burke

Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density.

Orbital-free Bond Breaking via Machine Learning

no code implementations7 Jun 2013 John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke

Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.

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