activity
20142021
most citedBy-passing the Kohn-Sham equations with machine learning

729 citations · 1.1k across the 6 of their papers we have counts for

collaborators

7 papers

physics.chem-ph2021★ 12 cited

How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?

Bhupalee Kalita, Ryan Pederson, Jielun Chen +2

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…

physics.comp-ph2020★ 180 cited

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

Li Li, Stephan Hoyer, Ryan Pederson +4

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 architectur…

physics.comp-ph2018★ 11 cited

Efficient prediction of 3D electron densities using machine learning

Mihail Bogojeski, Felix Brockherde, Leslie Vogt-Maranto +4

The Kohn-Sham scheme of density functional theory is one of the most widely used methods to solve electronic structure problems for a vast variety of atomistic systems across diffe…

cond-mat.str-el2016★ 115 cited

Pure density functional for strong correlations and the thermodynamic limit from machine learning

Li Li, Thomas E. Baker, Steven R. White +1

We use density-matrix renormalization group, applied to a one-dimensional model of continuum Hamiltonians, to accurately solve chains of hydrogen atoms of various separations and n…

physics.comp-ph2016★ 729 cited

By-passing the Kohn-Sham equations with machine learning

Felix Brockherde, Leslie Vogt, Li Li +3

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, r…

physics.comp-ph2015★ 3 cited

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

Kevin Vu, John Snyder, Li Li +5

Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random samplin…

physics.chem-ph2014

Understanding Machine-learned Density Functionals

Li Li, John C. Snyder, Isabelle M. Pelaschier +6

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. The properties of different…