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Density functional calculations on atoms are often used for determining accurate initial guesses as well as generating various types of pseudopotential approximations and efficient atomic-orbital basis sets for polyatomic calculations. To…

Computational Physics · Physics 2023-10-30 Susi Lehtola

The realistic description of correlated electron systems has taken an important step forward a few years ago as the combination of density functional methods and the dynamical mean-field theory was conceived. This framework allows access to…

Strongly Correlated Electrons · Physics 2011-06-15 M. Karolak , T. O. Wehling , F. Lechermann , A. I. Lichtenstein

We present a real-space formulation and higher-order finite-difference implementation of periodic Orbital-free Density Functional Theory (OF-DFT). Specifically, utilizing a local reformulation of the electrostatic and kernel terms, we…

Computational Physics · Physics 2015-12-23 Swarnava Ghosh , Phanish Suryanarayana

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular…

Machine Learning · Statistics 2022-01-11 Alexander Goscinski , Félix Musil , Sergey Pozdnyakov , Michele Ceriotti

In a global numerical weather prediction (NWP) modeling framework we study the implementation of Gaussian uncertainty of individual particles into the assimilation step of a localized adaptive particle filter (LAPF). We obtain a local…

Applications · Statistics 2022-06-16 Anne Rojahn , Nora Schenk , Peter Jan van Leeuwen , Roland Potthast

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

In this thesis are shown developments in the random phase approximation (RPA) in the context of range-separated theories. We present advances in the formalism of the RPA in general, and particularly in the "dielectric matrix" formulation of…

Chemical Physics · Physics 2015-11-24 B. Mussard

We report on the implementation of the Wannier Functions (WFs) formalism within the full-potential linearized augmented plane wave method (FLAPW), suitable for bulk, film and one-dimensional geometries. The details of the implementation, as…

Materials Science · Physics 2008-07-21 F. Freimuth , Y. Mokrousov , D. Wortmann , S. Heinze , S. Blügel

We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bart\'{o}k et al., Phys. Rev. B 87, 184115 (2013)]. Our aim is to improve the computational efficiency of…

Computational Physics · Physics 2019-09-16 Miguel A. Caro

We introduce an active-space embedding framework for core-level spectroscopies that connects localized atomic multiplets to continuum resonances within a plane-wave DFT/PAW description. The approach is complementary to widely used…

Strongly Correlated Electrons · Physics 2026-04-17 Alessandro Mirone , Mauro Rovezzi , Christoph Sahle , Alessandro Longo

We present a quantum embedding method that allows for the calculation of local excited states embedded in a Kohn-Sham density functional theory (DFT) environment. Projection-based quantum embedding methodologies provide a rigorous framework…

Chemical Physics · Physics 2019-09-30 Xuelan Wen , Daniel S. Graham , Dhabih V. Chulhai , Jason D. Goodpaster

In scattered data approximation, the span of a finite number of translates of a chosen radial basis function is used as approximation space and the basis of translates is used for representing the approximate. However, this natural choice…

Numerical Analysis · Mathematics 2024-08-22 Helmut Harbrecht , Rüdiger Kempf , Michael Multerer

Learning PDE dynamics for fluids increasingly relies on neural operators and Transformer-based models, yet these approaches often lack interpretability and struggle with localized, high-frequency structures while incurring quadratic cost in…

Machine Learning · Computer Science 2026-02-26 Zhihao Li , Yu Feng , Zhilu Lai , Wei Wang

Interatomic potentials provide a means to simulate extended length and time scales that are outside the reach of ab initio calculations. The development of an interatomic potential for a particular material requires the optimization of the…

Materials Science · Physics 2023-03-14 Aparna P. A. Subramanyam , Jan Jenke , Alvin Noe Ladines , Ralf Drautz , Thomas Hammerschmidt

In this manuscript, we will introduce a recently developed program GPView, which can be used for wave function analysis and visualization. The wave function analysis module can calculate and generate 3D cubes for various types of molecular…

Chemical Physics · Physics 2020-01-08 Tian Shi , Ping Wang

We present a computational approach which is tailored for reducing the complexity of the description of extended systems at the density functional theory level. We define a recipe for generating a set of localized basis functions which are…

Materials Science · Physics 2019-04-16 Laura E. Ratcliff , Luigi Genovese

Gaussian Approximation Potentials are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models…

We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of forward-mode algorithmic differentiation (AD) and density-functional perturbation theory (DFPT). In the resulting AD-DFPT…

Materials Science · Physics 2025-12-23 Niklas Frederik Schmitz , Bruno Ploumhans , Michael F. Herbst

We have investigated plasma-surface interactions with molecular dynamics (MD) simulations. It, however, is high cost computation and is limited to simulations for materials of nanometer order. In order to overcome the limitation, a…

Materials Science · Physics 2010-05-20 Arimichi Takayama , Seiki Saito , Atsushi M. Ito , Takahiro Kenmotsu , Hiroaki Nakamura

The random phase approximation (RPA) has emerged as a prominent first-principles method in material science, particularly to study the adsorption and chemisorption of small molecules on surfaces. However, its widespread application is…

Materials Science · Physics 2025-09-01 Edoardo Spadetto , Pier Herman Theodoor Philipsen , Arno Förster , Lucas Visscher
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