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We present an implementation of time-dependent density-functional theory (TDDFT) in the linear response formalism enabling the calculation of low energy optical absorption spectra for large molecules and nanostructures. The method avoids…

We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a way of training…

Computational Physics · Physics 2020-12-14 Yixiao Chen , Linfeng Zhang , Han Wang , E Weinan

A new framework is presented for evaluating the performance of self-consistent field methods in Kohn-Sham density functional theory. The aims of this work are two-fold. First, we explore the properties of Kohn-Sham density functional theory…

Computational Physics · Physics 2019-07-18 Nick Woods , Phil Hasnip , Mike Payne

For gradient flows, the existing structure-preserving schemes are difficult to achieve arbitrary high-order accuracy in time while preserving maximum-principle (MBP) and energy dissipating simultaneously. In this paper, we develop a new…

Numerical Analysis · Mathematics 2025-11-04 Qing Cheng , Tingfeng Wang , Xiaofei Zhao

A local discontinuous Galerkin (LDG) method for approximating large deformations of prestrained plates is introduced and tested on several insightful numerical examples in our previous computational work. This paper presents a numerical…

Numerical Analysis · Mathematics 2021-12-20 Andrea Bonito , Diane Guignard , Ricardo Nochetto , Shuo Yang

Minimizing functionals in the space of probability distributions can be done with Wasserstein gradient flows. To solve them numerically, a possible approach is to rely on the Jordan-Kinderlehrer-Otto (JKO) scheme which is analogous to the…

Machine Learning · Computer Science 2022-11-16 Clément Bonet , Nicolas Courty , François Septier , Lucas Drumetz

In recent years, deep learning methods, exemplified by Physics-Informed Neural Networks (PINNs), have been widely applied to the numerical solution of differential equations. However, these methods may suffer from limited accuracy, high…

Numerical Analysis · Mathematics 2026-03-17 Tao Tang , Jiang Yang , Yuxiang Zhao , Quanhui Zhu

Given the time-evolution of an electron charge density, the local potential in Kohn-Sham time-dependent density functional theory (KS-TDDFT) can be modeled as a sum of instantaneous and dynamic contributions by assuming a certain form of…

Computational Physics · Physics 2016-11-09 R. J. Magyar

It is shown here that Kohn-Sham equations cannot be derived from Hohenberg-Kohn theory without an additional postulate. Assuming that a functional derivative with respect to total electron density exists leads in general to a theory…

Condensed Matter · Physics 2007-05-23 R. K. Nesbet

A new method ( PI-DFT ) which combines path integrals and density functional theory is proposed as a pathway to many fields of physics. Within path integral theory it is possible to construct particle densities without explicitly…

Condensed Matter · Physics 2007-05-23 Peter Borrmann

We introduce an energy-based model, which seems especially suited for constrained systems. The proposed model provides an alternative to the popular port-Hamiltonian framework and exhibits similar properties such as energy dissipation as…

Numerical Analysis · Mathematics 2024-12-10 R. Altmann , P. Schulze

We study an implicit finite-volume scheme for non-linear, non-local aggregation-diffusion equations which exhibit a gradient-flow structure, recently introduced by Bailo, Carrillo, and Hu (2020). Crucially, this scheme keeps the dissipation…

Numerical Analysis · Mathematics 2022-04-19 Rafael Bailo , Jose A. Carrillo , Hideki Murakawa , Markus Schmidtchen

We present a new theory for partitioning simulations of periodic and solid-state systems into physically sound atomic contributions at the level of Kohn-Sham density functional theory. Our theory is based on spatially localized linear…

Chemical Physics · Physics 2024-10-01 Luna Zamok , Janus J. Eriksen

A generalization of the Kohn--Sham approach is derived where the correlation-energy functional depends on the one-particle density matrix of noninteracting states and on the external potential from the interacting target-state. The…

Chemical Physics · Physics 2007-05-23 James P. Finley

This paper gives an introduction to the Keldysh formalism, with emphasis on its usefulness in time-dependent density functional theory. In the first part we introduce the Keldysh contour and the one-particle Green function defined on this…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 Robert van Leeuwen , Nils Erik Dahlen , Gianluca Stefanucci , Carl-Olof Almbladh , Ulf von Barth

This paper develops the so-called Weighted Energy-Dissipation (WED) variational approach for the analysis of gradient flows in metric spaces. This focuses on the minimization of the parameter-dependent global-in-time functional of…

Analysis of PDEs · Mathematics 2018-01-17 Riccarda Rossi , Giuseppe Savaré , Antonio Segatti , Ulisse Stefanelli

We provide a new formulation of Time-Dependent Density Functional Theory (TDDFT) based on the geometric structure of the set of states constrained to have a fixed density. Orbital-free TDDFT is formulated using a hydrodynamics equation…

The rectangular collocation approach makes it possible to solve the Schr\"odinger equation with basis functions that do not have amplitude in all regions in which wavefunctions have significant amplitude. Collocation points can be…

Computational Physics · Physics 2020-07-01 Jonas Ku , Aditya Kamath , Tucker Carrington , Sergei Manzhos

In this work, we develop a novel numerical scheme to solve the classical Keller--Segel (KS) model which simultaneously preserves its intrinsic mathematical structure and achieves optimal accuracy. The model is reformulated into a gradient…

Numerical Analysis · Mathematics 2025-09-23 X. Yin , X. Lan , Y. Qin

We present a novel approximate inference method for diffusion processes, based on the Wasserstein gradient flow formulation of the diffusion. In this formulation, the time-dependent density of the diffusion is derived as the limit of…

Machine Learning · Statistics 2018-06-13 Charlie Frogner , Tomaso Poggio