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Over the last few decades, classical density-functional theory (DFT) and its dynamic extensions (DDFTs) have become powerful tools in the study of colloidal fluids. Recently, previous DDFTs for spherically-symmetric particles have been…

Statistical Mechanics · Physics 2016-08-02 Miguel A. Durán-Olivencia , Benjamin D. Goddard , Serafim Kalliadasis

Ensemble Density Functional Theory (EDFT) is a generalization of ground-state Density Functional Theory (GS DFT), which is based on an exact formal theory of finite collections of a system's ground and excited states. EDFT in various forms…

Materials Science · Physics 2024-08-27 Remi J. Leano , Aurora Pribram-Jones , David A. Strubbe

Photoionization dynamics of the RNA base Uracil is studied in the framework of Density Functional Theory (DFT). The photoionization calculations take advantage of a newly developed parallel version of a multicentric approach to the…

Chemical Physics · Physics 2009-11-13 D. Toffoli , P. Decleva , F. A. Gianturco , R. R. Lucchese

Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain key materials' properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To…

Materials Science · Physics 2023-05-29 Bruno Focassio , Michelangelo Domina , Urvesh Patil , Adalberto Fazzio , Stefano Sanvito

We present an empirical validation of the directional non-commutative monoidal embedding framework recently introduced in prior work~\cite{Godavarti2025monoidal}. This framework defines learnable compositional embeddings using distinct…

Machine Learning · Computer Science 2025-06-05 Mahesh Godavarti

The combination of density functional theory with dynamical mean-field theory (DFT+DMFT) has become a powerful first-principles approach to tackle strongly correlated materials in condensed matter physics. The wide use of this approach…

Strongly Correlated Electrons · Physics 2022-05-10 Xin Qu , Peng Xu , Rusong Li , Gang Li , Lixin He , Xinguo Ren

We develop a stochastic formulation of the optimally-tuned range-separated hybrid density functional theory which enables significant reduction of the computational effort and scaling of the non-local exchange operator at the price of…

Chemical Physics · Physics 2016-09-28 Daniel Neuhauser , Eran Rabani , Yael Cytter , Roi Baer

Density functional theory (DFT) underpins modern atomistic simulations of transition-metal surfaces. It can predict key properties linked to catalytic performance, such as adsorption energies and barrier heights, enabling new paradigms in…

Materials Science · Physics 2026-03-23 Benjamin X. Shi , Timothy C. Berkelbach

Density Functional Theory (DFT) is one of the most used ab initio theoretical frameworks in materials science. It derives the ground state properties of a multi-atomic ensemble directly from the computation of its one-particle density \nr…

Computational Physics · Physics 2015-05-30 Edoardo Di Napoli , Stefan Blügel , Paolo Bientinesi

This chapter provides a basic introduction to excited-state extensions of density functional theory (DFT), including time-dependent (TD-)DFT in both its linear-response and its explicitly time-dependent formulations. As applied to the…

Chemical Physics · Physics 2023-05-02 John M. Herbert

Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework…

Chemical Physics · Physics 2024-11-07 Jinni Yang , Runtong Pan , Jikai Sun , Jianzhong Wu

The constrained electron density method of embedding a Kohn-Sham system in a substrate system (first described by P. Cortona, Phys. Rev. B {\bf 44}, 8454 (1991) and T.A. Wesolowski and A. Warshel, J. Phys. Chem {\bf 97}, 8050 (1993)) is…

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

Electron dispersion forces play a crucial role in determining the structure and properties of biomolecules, molecular crystals and many other systems. However, an accurate description of dispersion is highly challenging, with the most…

Materials Science · Physics 2013-01-30 Jiří Klimeš , Angelos Michaelides

In order to describe the nonisothermal dynamics of two-phase flows or binary mixtures such as colloidal suspensions consisting of colloidal particles and solvent on a microscopic level, we derive a new extended dynamical density functional…

Soft Condensed Matter · Physics 2026-03-06 Michael te Vrugt , Hartmut Löwen , Helmut R. Brand , Raphael Wittkowski

Nowdays, modern microscopic approaches for fission are generally based on the framework of nuclear density functional theory (DFT), which has enabled a self-consistent treatment of both static and dynamic aspects of fission. The key issue…

Nuclear Theory · Physics 2023-09-08 Zeyu Li , Shengyuan Chen , Minghui Zhou , Yongjing Chen , Zhipan Li

To explore the applicability of orbital-free density functional theory (OF-DFT) in nuclear physics, we perform a systematic benchmark of 36 one-point kinetic energy density functionals, which are originally developed for electron systems in…

Nuclear Theory · Physics 2026-05-20 Tian Shuai Shang , Jian Li , Haozhao Liang , Xinhui Wu , Cheng Ma , Wenhui Mi , Xuecheng Shao , Yanchao Wang

We present an accurate, efficient and massively parallel finite-element code, DFT-FE, for large-scale ab-initio calculations (reaching $\sim 100,000$ electrons) using Kohn-Sham density functional theory (DFT). DFT-FE is based on a local…

Computational Physics · Physics 2020-01-08 Phani Motamarri , Sambit Das , Shiva Rudraraju , Krishnendu Ghosh , Denis Davydov , Vikram Gavini

Using dynamical density functional theory (DDFT) methods we investigate the laning instability of a sheared colloidal suspension. The nonequilibrium ordering at the laning transition is driven by non-affine particle motion arising from…

Soft Condensed Matter · Physics 2018-01-03 Alberto Scacchi , Andrew J. Archer , Joseph M. Brader

Linear scaling methods for density-functional theory (DFT) simulations are formulated in terms of localised orbitals in real-space, rather than the delocalised eigenstates of conventional approaches. In local-orbital methods, relative to…

Materials Science · Physics 2011-05-30 N. D. M. Hine , M. Robinson , P. D. Haynes , C. -K. Skylaris , M. C. Payne , A. A. Mostofi

Efficient sampling from un-normalized target distributions is pivotal in scientific computing and machine learning. While neural samplers have demonstrated potential with a special emphasis on sampling efficiency, existing neural implicit…

Machine Learning · Computer Science 2024-11-05 Weijian Luo , Wei Deng
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