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Related papers: Projected site-occupation embedding theory

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We present a quantum embedding methodology to resolve the Anderson impurity model in the context of dynamical mean-field theory, based on an extended exact diagonalization method. Our method provides a maximally localized quantum impurity…

Strongly Correlated Electrons · Physics 2021-02-19 Carla Lupo , François Jamet , Terence Tse , Ivan Rungger , Cedric Weber

Frozen Density Embedding Theory (FDET) [Wesolowski {\it Phys. Rev. A} {\bf 77}, 012504 (2008)] provides the interpretation of the eigenvalue equations for an embedded $N'$-electron wavefunction, in which the embedding operator is…

Chemical Physics · Physics 2025-07-02 Tomasz Adam Wesolowski

Embedded density functional theory (e-DFT) is used to describe the electronic structure of strongly interacting molecular subsystems. We present a general implementation of the Exact Embedding (EE) method [J. Chem. Phys. 133, 084103 (2010)]…

Other Condensed Matter · Physics 2011-07-27 Jason D. Goodpaster , Taylor A. Barnes , Thomas F. Miller

This article provides the first mathematical analysis of the Density Matrix Embedding Theory (DMET) method. We prove that, under certain assumptions, (i) the exact ground-state density matrix is a fixed-point of the DMET map for…

Mathematical Physics · Physics 2023-10-03 Eric Cancès , Fabian M. Faulstich , Alfred Kirsch , Eloïse Letournel , Antoine Levitt

Within the Composite Operator Method (COM), we report the solution of the Emery model (also known as p-d or three band model), which is relevant for the cuprate high-Tc superconduc- tors. We also discuss the relevance of the often-neglected…

Strongly Correlated Electrons · Physics 2013-06-17 Adolfo Avella , Ferdinando Mancini , Francesco Paolo Mancini , Evgeny Plekhanov

An alternative to Density Functional Theory are wavefunction based electronic structure calculations for solids. In order to perform them the Exponential Wall (EW) problem has to be resolved. It is caused by an exponential increase of the…

Chemical Physics · Physics 2017-06-07 Peter Fulde , Hermann Stoll

One of the goals in the development of large scale electronic structure methods is to perform calculations explicitly for a localised region of a system, while still taking into account the rest of the system outside of this region. An…

Materials Science · Physics 2009-10-01 J. R. Trail , D. M. Bird

The self-energy-functional approach proposed recently is applied to the single-band Hubbard model at half-filling to study the Mott-Hubbard metal-insulator transition within the most simple but non-trivial approximation. This leads to a…

Strongly Correlated Electrons · Physics 2007-05-23 M. Potthoff

Objective: Inclusion of individualised electrical conductivities of head tissues is crucial for the accuracy of electrical source imaging techniques based on electro/magnetoencephalography and the efficacy of transcranial electrical…

We describe an efficient quantum embedding framework for realistic ab initio density matrix embedding (DMET) calculations in solids. We discuss in detail the choice of orbitals and mapping to a lattice, treatment of the virtual space and…

Strongly Correlated Electrons · Physics 2020-01-15 Zhi-Hao Cui , Tianyu Zhu , Garnet Kin-Lic Chan

Computing ground-state properties of molecules is a promising application for quantum computers operating in concert with classical high-performance computing resources. Quantum embedding methods are a family of algorithms particularly…

We construct fixed-point wave functions and exactly solvable commuting-projector Hamiltonians for a large class of bosonic symmetry-enriched topological (SET) phases, based on the concept of equivalent classes of symmetric local unitary…

Strongly Correlated Electrons · Physics 2017-09-08 Meng Cheng , Zheng-Cheng Gu , Shenghan Jiang , Yang Qi

We extend our density matrix embedding theory (DMET) [Phys. Rev. Lett. 109 186404 (2012)] from lattice models to the full chemical Hamiltonian. DMET allows the many-body embedding of arbitrary fragments of a quantum system, even when such…

Strongly Correlated Electrons · Physics 2013-11-15 Gerald Knizia , Garnet Kin-Lic Chan

This research presents a novel depth estimation algorithm based on a Transformer-encoder architecture, tailored for the NYU and KITTI Depth Dataset. This research adopts a transformer model, initially renowned for its success in natural…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Linhan Xia , Junbang Liu , Tong Wu

Density matrix embedding theory (DMET) provides a framework to describe ground-state expectation values in strongly correlated systems, but its extension to dynamical quantities is still an open problem. We show one route to obtaining…

Strongly Correlated Electrons · Physics 2025-11-13 Shuoxue Li , Chenghan Li , Huanchen Zhai , Garnet Kin-Lic Chan

Noncollinear (NC) magnetism and spin-orbit coupling (SOC) are indispensable for predictive ab initio materials simulations with pronounced relativistic effects and magnetic frustration, yet they significantly increase the cost of…

The orbital degrees of freedom are of vital importance in explanation of various phenomena. Among them is the orbital-selective Mott transition (OSMT), which is thought to occur in several materials as Ca$_{2-x}$Sr$_x$RuO$_4$ and…

Strongly Correlated Electrons · Physics 2015-11-24 E Plekhanov , A Avella , F Mancini , F P Mancini

We obtain a general concept of triplet of Hilbert spaces with closed (unbounded) embeddings instead of continuous (bounded) ones. The construction starts with a positive selfadjoint operator $H$, that is called the Hamiltonian of the…

Functional Analysis · Mathematics 2025-11-04 Petru Cojuhari , Aurelian Gheondea

Doping a Mott insulator gives rise to unconventional superconducting correlations. Here we address the interplay between d-wave superconductivity and Mott physics using the two-dimensional Hubbard model with cellular dynamical mean-field…

Strongly Correlated Electrons · Physics 2023-08-30 C. Walsh , M. Charlebois , P. Sémon , A. -M. S. Tremblay , G. Sordi

Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches remains challenging due to limited multi-input and multi-scale…

Machine Learning · Computer Science 2025-08-12 Yichen Luo , Jia Wang , Dapeng Lan , Yu Liu , Zhibo Pang