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We showcase the advantages of orbital-free density-potential functional theory (DPFT), a more flexible variant of Hohenberg-Kohn density functional theory. DPFT resolves the usual trouble with the gradient-expanded kinetic energy functional…

Quantum Gases · Physics 2021-06-16 Martin-Isbjörn Trappe , Jun Hao Hue , Berthold-Georg Englert

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…

In this study, we propose a quantum-classical hybrid scheme for performing orbital-free density functional theory (OFDFT) using probabilistic imaginary-time evolution (PITE), designed for the era of fault-tolerant quantum computers (FTQC),…

Quantum Physics · Physics 2024-07-24 Yusuke Nishiya , Hirofumi Nishi , Taichi Kosugi , Yu-ichiro Matsushita

The combination of density functional theory with dynamical mean-field theory (DFT+DMFT) has become a powerful first-principles approach to tackle strongly correlated materials in condensed matter physics. The wide use of this approach…

Strongly Correlated Electrons · Physics 2022-05-10 Xin Qu , Peng Xu , Rusong Li , Gang Li , Lixin He , Xinguo Ren

Density Functional Theory (DFT) allows for predicting all the chemical and physical properties of molecular systems from first principles by finding an approximate solution to the many-body Schr\"odinger equation. However, the cost of these…

Machine Learning · Computer Science 2025-06-03 Majdi Hassan , Cristian Gabellini , Hatem Helal , Dominique Beaini , Kirill Neklyudov

Normalizing Flows (NF) are Generative models which transform a simple prior distribution into the desired target. They however require the design of an invertible mapping whose Jacobian determinant has to be computable. Recently introduced,…

Machine Learning · Computer Science 2025-09-18 Vincent Souveton , Arnaud Guillin , Jens Jasche , Guilhem Lavaux , Manon Michel

In this work, a unified representation of all the time-varying dynamics is accomplished with a Lagrangian framework for analyzing Fisher-Rao regularized dynamical optimal mass transport (OMT) derived flows. While formally equivalent to the…

Fluid Dynamics · Physics 2020-05-21 Rena Elkin , Saad Nadeem , Hedok Lee , Helene Benveniste , Allen Tannenbaum

To explore the applicability of orbital-free density functional theory (OF-DFT) in nuclear physics, we perform a systematic benchmark of 36 one-point kinetic energy density functionals, which are originally developed for electron systems in…

Nuclear Theory · Physics 2026-05-20 Tian Shuai Shang , Jian Li , Haozhao Liang , Xinhui Wu , Cheng Ma , Wenhui Mi , Xuecheng Shao , Yanchao Wang

The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (potential energy). These combinations yield different…

Machine Learning · Computer Science 2024-07-04 Kirill Neklyudov , Rob Brekelmans , Alexander Tong , Lazar Atanackovic , Qiang Liu , Alireza Makhzani

The classical Density Functional Theory (DFT) is introduced as an application of entropic inference for inhomogeneous fluids at thermal equilibrium. It is shown that entropic inference reproduces the variational principle of DFT when…

Statistical Mechanics · Physics 2021-09-14 Ahmad Yousefi , Ariel Caticha

Density functional theory (DFT) is routinely employed in material science and in quantum chemistry to simulate weakly correlated electronic systems. Recently, deep learning (DL) techniques have been adopted to develop promising functionals…

Strongly Correlated Electrons · Physics 2023-10-02 Emanuele Costa , Rosario Fazio , Sebastiano Pilati

The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent DFT Hamiltonian (DeepH) of…

Materials Science · Physics 2023-01-02 He Li , Zun Wang , Nianlong Zou , Meng Ye , Runzhang Xu , Xiaoxun Gong , Wenhui Duan , Yong Xu

Normalizing Flows (NFs) are universal density estimators based on Neural Networks. However, this universality is limited: the density's support needs to be diffeomorphic to a Euclidean space. In this paper, we propose a novel method to…

Machine Learning · Computer Science 2022-02-02 Christian Horvat , Jean-Pascal Pfister

The density matrix renormalization group (DMRG) method has already proved itself as a very efficient and accurate computational method, which can treat large active spaces and capture the major part of strong correlation. Its application on…

Chemical Physics · Physics 2022-10-31 Pavel Beran , Katarzyna Pernal , Fabijan Pavosevic , Libor Veis

A normalizing flow is an invertible mapping between an arbitrary probability distribution and a standard normal distribution; it can be used for density estimation and statistical inference. Computing the flow follows the change of…

Machine Learning · Computer Science 2021-12-10 Derek Onken , Samy Wu Fung , Xingjian Li , Lars Ruthotto

Orbital-free (OF) methods promise significant speed-up of computations based on density functional theory (DFT). In this field, the development of accurate kinetic-energy density functionals remains an open question. In this chapter we…

Materials Science · Physics 2013-05-03 Constantine Yannouleas , Uzi Landman

Continuous normalizing flows (CNFs) construct invertible mappings between an arbitrary complex distribution and an isotropic Gaussian distribution using Neural Ordinary Differential Equations (neural ODEs). It has not been tractable on…

Computer Vision and Pattern Recognition · Computer Science 2022-03-29 Shian Du , Yihong Luo , Wei Chen , Jian Xu , Delu Zeng

Orbital-free density functional theory as an extension of traditional Thomas-Fermi theory has attracted a lot of interest in the past decade because of developments in both more accurate kinetic energy functionals and highly efficient…

Materials Science · Physics 2009-11-10 Hong Jiang , Weitao Yang

Over the last few decades, classical density-functional theory (DFT) and its dynamic extensions (DDFTs) have become powerful tools in the study of colloidal fluids. Recently, previous DDFTs for spherically-symmetric particles have been…

Statistical Mechanics · Physics 2016-08-02 Miguel A. Durán-Olivencia , Benjamin D. Goddard , Serafim Kalliadasis

We present an accurate and efficient real-space Density Functional Theory (DFT) framework for the ab-initio study of non-orthogonal crystal systems. Specifically, employing a local reformulation of the electrostatics, we develop a novel…

Computational Physics · Physics 2018-05-09 Abhiraj Sharma , Phanish Suryanarayana