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Quantum computers open up new avenues for modelling the physical properties of materials and molecules. Density Functional Theory (DFT) is the gold standard classical algorithm for predicting these properties, but relies on approximations…

Quantum Physics · Physics 2024-02-29 Evan Sheridan , Lana Mineh , Raul A. Santos , Toby Cubitt

The density functional theory (DFT) is the most versatile electronic structure method used in quantum chemical calculations, and is increasingly applied in astrochemical research. This mini-review provides an overview of the applications of…

Astrophysics of Galaxies · Physics 2023-10-24 Qingli Liao , Junzhi Wang , Peng Xie , Enwei Liang , Zhao Wang

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z=118), it is estimated that as many as about 3000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but…

The presence of point defects such as vacancies plays an important role in material design. Here, we demonstrate that a graph neural network (GNN) model trained only on perfect materials can also be used to predict vacancy formation…

Materials Science · Physics 2024-02-19 Kamal Choudhary , Bobby G. Sumpter

We suggest to include the density of electron charge explicitly in the electron potential of density functional theory, rather than implicitly via exchange-correlation functionals. The advantages of the approach are conceptual and…

Materials Science · Physics 2007-05-23 Werner A. Hofer , Krisztian Palotas

Density Functional Theory (DFT) is one of the most widely used methods for "ab initio" calculations of the structure of atoms, molecules, crystals, surfaces, and their interactions. Unfortunately, the customary introduction to DFT is often…

Physics Education · Physics 2010-12-07 Nathan Argaman , Guy Makov

The saturation of symmetric nuclear matter -- reflected in the nearly constant interior density of heavy nuclei -- is a defining property of nuclear matter. Modern relativistic energy density functionals (EDFs) calibrated exclusively to the…

Nuclear Theory · Physics 2025-12-30 Md Jafrul Islam , Athul Kunjipurayil , J. Piekarewicz , A. Volya

Density functional theory (DFT) is an exact alternative formulation of quantum mechanics, in which it is possible to calculate the total energy, the spin and the charge density of many-electron systems in the ground state. In practice, it…

Atomic Physics · Physics 2014-03-25 Uri Argaman , Guy Makov , Eli Kraisler

Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been…

The recent fabrication of graphene nanoribbon (GNR) field-effect transistors poses a challenge for first-principles modeling of carbon nanoelectronics due to many thousand atoms present in the device. The state of the art quantum transport…

Mesoscale and Nanoscale Physics · Physics 2010-04-28 Denis A. Areshkin , Branislav K. Nikolic

Electronic density of states (DOS) is a key factor in condensed matter physics and material science that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain…

Materials Science · Physics 2019-04-12 Byung Chul Yeo , Donghun Kim , Chansoo Kim , Sang Soo Han

We discuss the construction of a nuclear Energy Density Functional (EDF) from ab initio calculations, and we advocate the need of a methodical approach that is free from ad hoc assumptions. The equations of state (EoS) of symmetric nuclear…

Nuclear Theory · Physics 2021-08-18 F. Marino , C. Barbieri , G. Colò , A. Lovato , F. Pederiva , X. Roca-Maza , E. Vigezzi

Orbital-free Density Functional Theory (OF-DFT) has been used when studying atoms, molecules and solids. In nuclear physics, there has been basically no application of OF-DFT so far, as the Density Functional Theory (DFT) has been widely…

Nuclear Theory · Physics 2023-08-03 Gianluca Colo' , Kouichi Hagino

This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of…

Disordered Systems and Neural Networks · Physics 2024-12-20 Selva Chandrasekaran Selvaraj

Due to its favorable computational efficiency time-dependent (TD) density functional theory (DFT) enables the prediction of electronic spectra in a high-throughput manner across chemical space. Its predictions, however, can be quite…

The electric dipole (E1) strength functions have been systematically calculated based on the time-dependent density functional theory (TDDFT), using the finite amplitude method and the real-time approach to the TDDFT with pairing…

Nuclear Theory · Physics 2017-08-23 Shuichiro Ebata , Tsunenori Inakura , Takashi Nakatsukasa

The key features of density-functional theory (DFT) within a minimalistic implementation of quantum electrodynamics are demonstrated, thus allowing to study elementary properties of quantum-electrodynamical density-functional theory…

Fundamentals of energy density functional in nuclear physics are presented. Much attention is paid to a mathematically rigorous treatment of deriving the energy density functional. The specific features of the density functional used in…

Nuclear Theory · Physics 2012-11-30 Yoritaka Iwata , Joachim A. Maruhn

Electromagnetic interactions serve as essential probes for studying and testing our understanding of the atomic nucleus, as they reveal emergent properties across the nuclear chart. We analyse their corresponding observables, which relate…

Nuclear Theory · Physics 2026-05-13 J. Dobaczewski , B. C. Backes , R. P. de Groote , A. Restrepo-Giraldo , X. Sun , H. Wibowo

We present an accurate local density-functional for electronic-structure calculations within the density functional theory (DFT). The functional is derived by analyzing the structure of the standard perturbative expansion of the correlation…

Materials Science · Physics 2024-11-28 Mario Benites , Angel Rosado , Efstratios Manousakis