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Solving partial differential equations (PDEs) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but…

Numerical Analysis · Mathematics 2025-01-28 Qi Wang , Yuan Mi , Haoyun Wang , Yi Zhang , Ruizhi Chengze , Hongsheng Liu , Ji-Rong Wen , Hao Sun

An extension of the Variational Quantum Eigensolver (VQE) method is presented where a quantum computer generates an accurate exchange-correlation potential for a Density Functional Theory (DFT) simulation on classical hardware. The method…

Quantum Physics · Physics 2019-03-14 Ryan Hatcher , Jorge A. Kittl , Christopher Bowen

We introduce an efficient finite-element approach for large-scale real-space pseudopotential density functional theory (DFT) calculations incorporating noncollinear magnetism and spin-orbit coupling. The approach, implemented within the…

Materials Science · Physics 2025-06-11 Nikhil Kodali , Phani Motamarri

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local density functional theory (DFT), and thereby furnish a more accurate and reliable description of the electronic structure in…

Computational Physics · Physics 2021-05-10 Hsin-Yu Ko , Junteng Jia , Biswajit Santra , Xifan Wu , Roberto Car , Robert A. DiStasio

A new approach for calculating spectral density functions of strongly correlated electron systems is proposed within the exact diagonalization method of dynamical mean-field theory (DMFT). This approach is based on the analytic continuation…

Strongly Correlated Electrons · Physics 2016-10-05 Song-Jin O , Hak-Chol Pak , Kwang-Il Ryom , Song-Jin Im

Real-Time Time-Dependent Density Functional Theory (TDDFT) has become an attractive tool to model quantum dynamics on a first-principles Density Functional Theory level. In recent years, several developments and applications in this field…

Computational Physics · Physics 2020-08-21 Joscha Hekele , Peter Kratzer

Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for…

Chemical Physics · Physics 2020-11-17 Masashi Tsubaki , Teruyasu Mizoguchi

A recently developed dynamical mean-field theory in the iterated perturbation theory approximation was used as a basis for construction of the "first principles" calculation scheme for investigating electronic structure of strongly…

Strongly Correlated Electrons · Physics 2009-10-30 V. I. Anisimov , A. I. Poteryaev , M. A. Korotin , A. O. Anokhin , G. Kotliar

This review presents a concise, yet comprehensive discussion on the evolution of theoretical methods employed to determine the ground and excited states of molecules in weak and strong magnetic fields. The weak-field cases have been studied…

Atomic and Molecular Clusters · Physics 2021-05-31 Mihir Date , R. W. A Havenith

The development of systematic effective field theories (EFTs) for nuclear forces and advances in solving the nuclear many-body problem have greatly improved our understanding of dense nuclear matter and the structure of finite nuclei. For…

Nuclear Theory · Physics 2025-09-30 Brendan T. Reed , Matthias Heinz , Pierre Arthuis , Achim Schwenk , Ingo Tews

Over many years, computational simulations based on Density Functional Theory (DFT) have been used extensively to study many different materials at the atomic scale. However, its application is restricted by system size, leaving a number of…

Mesoscale and Nanoscale Physics · Physics 2018-12-05 Carlos Romero-Muñiz , Ayako Nakata , Pablo Pou , David R. Bowler , Tsuyoshi Miyazaki , Rubén Pérez

We report a local, weight-dependent correlation density-functional approximation that incorporates information about both ground and excited states in the context of density-functional theory for ensembles (eDFT). This density-functional…

Chemical Physics · Physics 2020-06-05 Pierre-François Loos , Emmanuel Fromager

Time-resolved photoemission spectroscopy provides a unique and direct way to explore the real-time nonequilibrium dynamics of electrons and holes. The formal theory of the spectral function evolution requires inclusion of electronic…

Materials Science · Physics 2025-05-09 Thomas Blommel , Enrico Perfetto , Gianluca Stefanucci , Vojtěch Vlček

The LDA+DMFT method is a very powerful tool for gaining insight into the physics of strongly correlated materials. It combines traditional ab-initio density-functional techniques with the dynamical mean-field theory. The core aspects of the…

Strongly Correlated Electrons · Physics 2017-11-22 Eva Pavarini

Dynamical mean-field theory (DMFT) is a non-perturbative technique for the investigation of correlated electron systems. Its combination with the local density approximation (LDA) has recently led to a material-specific computational scheme…

Strongly Correlated Electrons · Physics 2009-11-10 D. Vollhardt , K. Held , G. Keller , R. Bulla , Th. Pruschke , I. A. Nekrasov , V. I. Anisimov

Excitation energies of light-emitting organic conjugated polymers have been investigated with time-dependent density functional theory (TDDFT) within the adiabatic approximation for the dynamical exchange-correlation potential. Our…

Materials Science · Physics 2009-07-06 Jianmin Tao , Sergei Tretiak , Jian-Xin Zhu

Density functional theory (DFT) is one of the main methods in Quantum Chemistry that offers an attractive trade off between the cost and accuracy of quantum chemical computations. The electron density plays a key role in DFT. In this work,…

Chemical Physics · Physics 2018-09-11 Anton V. Sinitskiy , Vijay S. Pande

A novel approach to electronic correlations and magnetism of crystals based on realistic electronic structure calculations is reviewed. In its simplest form it is a combination of the ``local density approximation'' (LDA) and the dynamical…

Strongly Correlated Electrons · Physics 2007-05-23 A. I. Lichtenstein , M. I. Katsnelson , G. Kotliar

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be…

The performance of time-independent, orbital optimized calculations of excited states is assessed with respect to charge transfer excitations in organic molecules in comparison to the linear-response time-dependent density functional theory…

Chemical Physics · Physics 2024-05-22 Elli Selenius , Alec Elías Sigurdarson , Yorick L. A. Schmerwitz , Gianluca Levi
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