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Faithful representations of atomic environments and general models for regression can be harnessed to learn electron densities that are close to the ground state. One of the applications of data-derived electron densities is to orbital-free…

Materials Science · Physics 2019-03-01 Andrew T. Fowler , Chris J. Pickard , James A. Elliott

Orbital-free density functional theory (OF-DFT) constitutes a computationally highly effective tool for modeling electronic structures of systems ranging from room-temperature materials to warm dense matter. Its accuracy critically depends…

A single-term density functional model for nondynamic and strong correlation is presented, based on single-determinant Kohn-Sham density functional theory. It is derived from modeling the adiabatic connection and contains only two nonlinear…

Chemical Physics · Physics 2015-12-21 Jing Kong , Emil Proynov

Semi-local density functionals for the exchange-correlation energy of electrons are extensively used as it produce realistic and accurate results for finite and extended systems. The choice of techniques play crucial role in constructing…

Materials Science · Physics 2017-11-01 Subrata Jana , Prasanjit Samal

We formulate the Kohn-Sham density functional theory (KS-DFT) as a statistical theory in which the electron density is deter-mined from an average of correlated stochastic densities in a trace formula. The key idea is that it is sufficient…

Materials Science · Physics 2015-06-15 Roi Baer , Daniel Neuhauser , Eran Rabani

The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems.…

Quantum Physics · Physics 2023-08-23 Jun Yang , James D Whitfield

Classical density functional theory (cDFT) and dynamical density functional theory (DDFT) are modern statistical mechanical theories for modeling many-body colloidal systems at the one-body density level. The theories hinge on knowing the…

Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly…

Chemical Physics · Physics 2015-06-16 John C. Snyder , Matthias Rupp , Katja Hansen , Leo Blooston , Klaus-Robert Müller , Kieron Burke

Using principles of asymptotic analysis, we derive the exact leading corrections to the Thomas-Fermi kinetic energy approximation for Kohn-Sham electrons for slabs. This asymptotic expansion approximation includes crucial quantum…

Materials Science · Physics 2024-02-21 Pavel Okun , Antonio C. Cancio , Kieron Burke

The development of semilocal models for the kinetic energy density (KED) is an important topic in density functional theory (DFT). This is especially true for subsystem DFT, where these models are necessary to construct the required…

Other Condensed Matter · Physics 2017-02-15 S. Śmiga , E. Fabiano , L. A. Constantin , F. Della Sala

We train a neural network as the universal exchange-correlation functional of density-functional theory that simultaneously reproduces both the exact exchange-correlation energy and potential. This functional is extremely non-local, but…

Computational Physics · Physics 2019-10-10 Jonathan Schmidt , Carlos L. Benavides-Riveros , Miguel A. L. Marques

We find the analytical solution to the time-dependent density-functional theory (TDDFT) problem for the quasi-low-dimensional (2D and 1D) electron gas (QLDEG) with only one band filled in the direction perpendicular to the system extent.…

Mesoscale and Nanoscale Physics · Physics 2017-06-14 Vladimir U. Nazarov

The exchange-correlation energy in Kohn-Sham density functional theory is expressed as a functional of the electronic density and the Kohn-Sham orbitals. An alternative to Kohn-Sham theory is to express the energy as a functional of the…

Computational Physics · Physics 2009-10-31 S. Goedecker , C. Umrigar

Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a…

High Energy Physics - Phenomenology · Physics 2025-01-14 Nour Makke , Sanjay Chawla

Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with…

Computational Physics · Physics 2015-06-03 John C. Snyder , Matthias Rupp , Katja Hansen , Klaus-Robert Müller , Kieron Burke

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 different scientific fields. While the method is fast relative to…

We introduce surrogate functionals: machine-learned energy functionals for orbital-free density functional theory (OF-DFT) which are defined not by universal fidelity to a physical reference, but merely by the requirement that density…

Machine Learning · Computer Science 2026-04-23 Roman Remme , Fred A. Hamprecht

The positive definite Kohn-Sham kinetic energy(KS-KE) density plays crucial role in designing semilocal meta generalized gradient approximations(meta-GGAs) for low dimensional quantum systems. It has been rigorously shown that near nucleus…

Materials Science · Physics 2017-03-17 Subrata Jana , Prasanjit Samal

In the exact Kohn-Sham density-functional theory (DFT), the total energy versus the number of electrons is a series of linear segments between integer points. However, commonly used approximate density functionals produce total energies…

Materials Science · Physics 2013-04-03 Eli Kraisler , Leeor Kronik

The electron localization function (ELF) is a universal measure of electron localization that allows for, e.g., an effective characterization of physical bonds in molecular and solid state systems. In the context of the widely used…

Strongly Correlated Electrons · Physics 2019-08-20 Alexander Lindmaa , Joel Davidsson , Ann E. Mattsson , Rickard Armiento