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Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations --- alongside impressive results using machine learning techniques for computation --- hybridizing…

Quantum Physics · Physics 2018-10-24 Rongxin Xia , Sabre Kais

We present a method to discretize the Kohn-Sham Hamiltonian matrix in the pseudopotential framework by a small set of basis functions automatically contracted from a uniform basis set such as planewaves. Each basis function is localized…

Computational Physics · Physics 2015-06-03 Lin Lin , Lexing Ying

Quantum mechanical calculations for material modelling using Kohn-Sham density functional theory (DFT) involve the solution of a nonlinear eigenvalue problem for $N$ smallest eigenvector-eigenvalue pairs with $N$ proportional to the number…

Computational Physics · Physics 2023-09-26 Sameer Khadatkar , Phani Motamarri

A new method is presented for obtaining all-electron results from a pseudopotential calculation. This is achieved by carrying out a localised calculation in the region of an atomic nucleus using the embedding potential method of Inglesfield…

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

In this paper, the first large-scale application of multiscale-spectral generalized finite element methods (MS-GFEM) to composite aero-structures is presented. The crucial novelty lies in the introduction of A-harmonicity in the local…

Numerical Analysis · Mathematics 2023-03-03 Jean Bénézech , Linus Seelinger , Peter Bastian , Richard Butler , Timothy Dodwell , Chupeng Ma , Robert Scheichl

The ground state electron density -- obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations -- contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the…

We extend our density matrix embedding theory (DMET) [Phys. Rev. Lett. 109 186404 (2012)] from lattice models to the full chemical Hamiltonian. DMET allows the many-body embedding of arbitrary fragments of a quantum system, even when such…

Strongly Correlated Electrons · Physics 2013-11-15 Gerald Knizia , Garnet Kin-Lic Chan

Block copolymers provide a wonderful platform in studying the soft condensed matter systems. Many fascinating ordered structures have been discovered in bulk and confined systems. Among various theories, the self-consistent field theory…

Soft Condensed Matter · Physics 2019-05-01 Huayi Wei , Ming Xu , Wei Si , Kai Jiang

A novel embedded atom method (EAM) potential for the Xi-phases of Al-Pd-Mn has been determined with the force-matching method. Different combinations of analytic functions were tested for the pair and transfer part. The best results are…

Materials Science · Physics 2012-02-09 Daniel Schopf , Peter Brommer , Benjamin Frigan , Hans-Rainer Trebin

The theorems of density functional theory (DFT) and reduced density matrix functional theory (RDMFT) establish a bijective map between the external potential of a many-body system and its electron density or one-particle reduced density…

Chemical Physics · Physics 2023-02-22 Xuecheng Shao , Lukas Paetow , Mark E. Tuckerman , Michele Pavanello

We combine classical heuristics with partial shadow tomography to enable efficient protocols for extracting information from correlated ab initio electronic systems encoded on quantum devices. By proposing the use of a correlation energy…

We present recent developments of the NTChem program for performing large scale hybrid Density Functional Theory calculations on the supercomputer Fugaku. We combine these developments with our recently proposed Complexity Reduction…

We developed a method for calculating solid-state ground-state properties and fundamental band-gaps using a generalized Kohn-Sham approach combining a local density approximation (LDA) functional with a long-range explicit exchange orbital…

Materials Science · Physics 2019-03-27 Helen R. Eisenberg , Roi Baer

Extracting the Hamiltonian of interacting quantum-information processing systems is a keystone problem in the realization of complex phenomena and large-scale quantum computers. The remarkable growth of the field increasingly requires…

Conventional methods for modeling thermocatalytic systems are typically based on the Kohn-Sham density functional theory (KS-DFT), neglecting the inhomogeneous distributions of gas molecules in the reactive environment. However, industrial…

Chemical Physics · Physics 2025-08-14 Jikai Sun , Jianzhong Wu

We describe how to apply the recently developed pole expansion and selected inversion (PEXSI) technique to Kohn-Sham density function theory (DFT) electronic structure calculations that are based on atomic orbital discretization. We give…

Computational Physics · Physics 2015-06-04 Lin Lin , Mohan Chen , Chao Yang , Lixin He

We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in…

Chemical Physics · Physics 2021-11-10 Alan M. Lewis , Andrea Grisafi , Michele Ceriotti , Mariana Rossi

To study the fundamental physics of complex multiphase flow systems using advanced measurement techniques, especially the electrical capacitance tomography (ECT) approach, this article carries out an initial literature review of the ECT…

Computer Vision and Pattern Recognition · Computer Science 2015-10-16 Kezhi Li

Numerical simulations based on electronic structure calculations are finding ever growing applications in many areas of physics. A major limiting factor is however the cubic scaling of the algorithms used. Building on previous work [F. R.…

Materials Science · Physics 2009-11-11 Florian R. Krajewski , Michele Parrinello

Combining sum factorization, weighted quadrature, and row-based assembly enables efficient higher-order computations for tensor product splines. We aim to transfer these concepts to immersed boundary methods, which perform simulations on a…

Computational Engineering, Finance, and Science · Computer Science 2023-09-06 Benjamin Marussig , René Hiemstra , Dominik Schillinger
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