English
Related papers

Related papers: 5- and 6-membered rings: A natural orbital functio…

200 papers

We propose a hybrid approach which employs the dynamical mean-field theory (DMFT) self-energy for the correlated, typically rather localized orbitals and a conventional density functional theory (DFT) exchange-correlation potential for the…

Strongly Correlated Electrons · Physics 2021-06-16 Sumanta Bhandary , Karsten Held

The accurate calculation of the (differential) correlation energy is central to the quantum chemical description of bond-formation and bond-dissociation processes. In order to estimate the quality of single- and multi-reference approaches…

Chemical Physics · Physics 2013-07-16 Katharina Boguslawski , Pawel Tecmer , Gergely Barcza , Ors Legeza , Markus Reiher

We propose a variational wavefunction containing parameters to tune the probabilities of all the possible onsite configurations for the periodic Anderson model. We call it the full onsite-correlation wavefunction (FOWF). This is a simple…

Strongly Correlated Electrons · Physics 2016-12-01 Katsunori Kubo , Hiroaki Onishi

We propose a novel scheme to bring reduced density matrix functional theory (RDMFT) into the realm of density functional theory (DFT) that preserves the accurate density functional description at equilibrium, while incorporating accurately…

Chemical Physics · Physics 2014-11-07 Nektarios N. Lathiotakis , Nicole Helbig , Angel Rubio , Nikitas I. Gidopoulos

A non-relativisitic nuclear density functional theory is constructed, not as usual, from an effective density dependent nucleon-nucleon force but directly introducing in the functional results from microscopic nuclear and neutron matter…

Nuclear Theory · Physics 2008-11-26 M. Baldo , P. Schuck , X. Vinas

Recently, an approximate theoretical framework was introduced, called local reduced density matrix functional theory (local-RDMFT), where functionals of the one-body reduced density matrix (1-RDM) are minimized under the additional…

We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic…

Computational Physics · Physics 2019-11-27 Brandon Anderson , Truong-Son Hy , Risi Kondor

Quantifying correlation and entanglement between molecular orbitals can elucidate the role of quantum effects in strongly correlated reaction processes. However, accurately storing the wavefunction for a classical computation of those…

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

The high computational scaling with the number of correlated electrons and the size of the basis set is a bottleneck which limits applications of coupled cluster (CC) algorithms. This is particularly so for calculations based on 2- or…

Chemical Physics · Physics 2022-06-13 Xiang Yuan , Lucas Visscher , Andre Severo Pereira Gomes

Low-scaling electron correlation theory based on the pair natural orbital approximation, PNO-CCSD(T), has become a powerful computational tool. Motivated by the recent discovery of large errors for organometallic molecules, we assess the…

Chemical Physics · Physics 2023-10-10 Reka A. Horvath , Kesha Sorathia , Isabelle Saint , David P. Tew

Electron pairs have an illustrious history in chemistry, from powerful concepts to understanding structural stability and reactive changes, to the promise of serving as building blocks of quantitative descriptions of the electronic…

A proper theoretical description of electronic structure of the 3d orbitals in the metal centers of functional metalorganics is a challenging problem. In this letter, we apply density functional theory and an exact diagonalization method in…

Searches for pseudo-magnetic spin couplings require implementation of techniques capable of sensitive detection of such interactions. While Spin-Exchange Relaxation Free (SERF) magnetometry is one of the most powerful approaches enabling…

Instrumentation and Detectors · Physics 2022-01-14 Mikhail Padniuk , Marek Kopciuch , Riccardo Cipolletti , Arne Wickenbrock , Dmitry Budker , Szymon Pustelny

Electron correlations in molecules can be divided in short range dynamical correlations, long range Van der Waals type interactions and near degeneracy static correlations. In this work we analyze for a one-dimensional model of a…

Chemical Physics · Physics 2014-05-13 K. J. H. Giesbertz , R. van Leeuwen

Accurate computational predictions of metal-organic frameworks (MOFs) and their properties is crucial for discovering optimal compositions and applying them in relevant technological areas. This work benchmarks density functional theory…

Materials Science · Physics 2025-03-11 Joshua Edzards , Julia Santana Andreo , Holger-Dietrich Saßnick , Caterina Cocchi

We describe a method for imposing the correct electron-nucleus (e-n) cusp in molecular orbitals expanded as a linear combination of (cuspless) Gaussian basis functions. Enforcing the e-n cusp in trial wave functions is an important asset in…

Chemical Physics · Physics 2020-06-05 Pierre-François Loos , Anthony Scemama , Michel Caffarel

Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…

Materials Science · Physics 2024-05-13 Abhishek Sharma , Stefano Sanvito

We derive the non-adiabatic ring polymer molecular dynamics (RPMD) approach in the phase space of the SU(N) Lie Group. This method, which we refer to as the spin mapping non-adiabatic RPMD (SM-NRPMD), is based on the spin-mapping formalism…

Chemical Physics · Physics 2023-02-15 Duncan Bossion , Sutirtha N. Chowdhury , Pengfei Huo

This paper presents a permutation binary neural network characterized by local binary connection, global permutation connection, and the signum activation function. The dynamics is described by a difference equation of binary state…

Dynamical Systems · Mathematics 2022-01-25 Hotaka Udagawa , Taiji Okano , Toshimichi Saito