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Found 7 papers, 2 papers with code
Using Machine Learning to Find New Density Functionals
Machine learning has now become an integral part of research and innovation.
How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?
Kohn-Sham regularizer (KSR) is a differentiable machine learning approach to finding the exchange-correlation functional in Kohn-Sham density functional theory (DFT) that works for strongly correlated systems.
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
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
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 Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Accurate approximations to density functionals have recently been obtained via machine learning (ML).
Understanding Machine-learned Density Functionals
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
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.
