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An approximate but straight forward projection method to molecular many alpha-particle states is proposed and the overlap to the shell model space is determined. The resulting space is in accordance with the shell model, but still contains…

Nuclear Theory · Physics 2021-10-13 J. R. M. Berriel-Aguayo , P. O. Hess

We present a systematic, quasi-automated methodology for generating electronic models in the framework of second-principles density functional theory (SPDFT). This approach enables the construction of accurate and computationally efficient…

Despite their rich information content, electronic structure data amassed at high volumes in $ab$ $initio$ molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation…

Materials Science · Physics 2022-02-22 Qiangqiang Gu , Linfeng Zhang , Ji Feng

We apply density functional theory, in the local density approximation, to a quasi-one-dimensional electron gas in order to quantify the effect of Coulomb and correlation effects in modulating, and therefore patterning, the charge density…

Mesoscale and Nanoscale Physics · Physics 2016-11-23 E. T. Owen , C. H. W. Barnes

A key to building functional devices on the basis of single molecule magnets in the framework of molecular electronics is the ability to deposit and study these molecules on a surface, because the structural, electronic and magnetic…

Mesoscale and Nanoscale Physics · Physics 2016-01-26 Judith Donner , Jan-Philipp Broschinski , Bastian Feldscher , Anja Stammler , Hartmut Bögge , Thorsten Glaser , Daniel Wegner

A number of methods are discussed which may serve for a treatment of electron correlations in solids. When the electron correlations are relatively weak like in semiconductors or a number of ionic crystals one may start from a…

Condensed Matter · Physics 2007-05-23 Prof. Dr. Peter Fulde

We analyze a model problem representing a multi-electronic molecule sitting on a metal surface. Working with a reduced configuration interaction Hamiltonian, we show that one can extract very accurate ground state wavefunctions as compared…

Chemical Physics · Physics 2020-09-24 Junhan Chen , Zuxin Jin , Wenjie Dou , Joseph Subotnik

Most density functionals have been developed by imposing the known exact constraints on the exchange-correlation energy, or by a fit to a set of properties of selected systems, or by both. However, accurate modeling of the conventional…

Materials Science · Physics 2016-08-24 Jianmin Tao , Yuxiang Mo

Lack of memory (locality in time) is a major limitation of almost all present time-dependent density functional approximations. By using semiclassical dynamics to compute correlation effects within a density-matrix functional approach, we…

Chemical Physics · Physics 2015-05-18 A. K. Rajam , I. Raczkowska , N. T. Maitra

Highly accurate experimental structure factors of silicon are available in the literature, and these provide the ideal test for any \emph{ab initio} method for the construction of the all-electron charge density. In a recent paper [J. R.…

Materials Science · Physics 2015-05-14 J. R. Trail , D. M. Bird

Protein folding, peptide aggregation and crystallization, as well as adsorption of molecules on soft or solid substrates have an essential feature in common: In all these processes, structure formation is guided by a collective, cooperative…

Statistical Mechanics · Physics 2009-02-12 Michael Bachmann , Wolfhard Janke

A new scheme for constructing approximate effective electron potentials within density-functional theory is proposed. The scheme consists of calculating the effective potential for a series of reference systems, and then using these…

Condensed Matter · Physics 2016-08-14 K. Stokbro , N. Chetty , K. W. Jacobsen , J. K. Nørskov

Electron microscopy is a powerful tool for studying the properties of materials down to their atomic structure. In many cases, the quantitative interpretation of images requires simulations based on atomistic structure models. These…

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural…

Materials Science · Physics 2020-02-06 Sandip De , Albert P. Bartók , Gábor Csányi , Michele Ceriotti

The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to…

Chemical Physics · Physics 2022-06-29 Andrea Grisafi , Alan M. Lewis , Mariana Rossi , Michele Ceriotti

Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new,…

We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular…

Chemical Physics · Physics 2018-10-16 Matthew Welborn , Lixue Cheng , Thomas F. Miller

The toolbox for imaging molecules is well-equipped today. Some techniques visualize the geometrical structure, others the electron density or electron orbitals. Molecules are many-body systems for which the correlation between the…

A mean field rate theory description of the homo- and co-polymerization of $f$-functional molecules is developed, which contains the formation of short cyclic structures inside the network. The predictions of this model are compared with…

Soft Condensed Matter · Physics 2021-03-31 Michael Lang , Konrad Schwenke , Jens-Uwe Sommer

A model subspace configuration interaction method is developed to obtain chemically accurate electron correlations by diagonalising a very compact effective Hamiltonian of realistic molecule. The construction of the effective Hamiltonian is…

Chemical Physics · Physics 2022-10-18 Jiasheng Li , Jun Yang