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Accurate many-body treatments of condensed-phase systems are challenging because correlated solvers such as full configuration interaction (FCI) and the density matrix renormalization group (DMRG) scale exponentially with system size.…

We present a detailed analysis of numerical discreteness errors in two-species, gravity-only, cosmological simulations using the density power spectrum as a diagnostic probe. In a simple setup where both species are initialized with the…

Cosmology and Nongalactic Astrophysics · Physics 2023-05-10 Xin Liu , J. D. Emberson , Michael Buehlmann , Nicholas Frontiere , Salman Habib

This paper presents an approach for obtaining accurate interaction energies at the DFT level for systems where dispersion interactions are important. This approach combines Becke and Johnson's [J. Chem. Phys. 127, 154108 (2007)] method for…

We have performed {\it ab initio} calculations for a series of energetic solids to explore their structural and electronic properties. To evaluate the ground state volume of these molecular solids, different dispersion correction methods…

Materials Science · Physics 2015-09-29 S. Appalakondaiah , G. Vaitheeswaran , S. Lebègue

In this paper, a novel method to adaptively approximate the solution to stochastic differential equations, which is based on compressive sampling and sparse recovery, is introduced. The proposed method consider the problem of sparse…

Numerical Analysis · Mathematics 2013-07-03 Behrooz Azarkhalili

We study the dynamical density matrix renormalization group (DDMRG) and time-dependent density matrix renormalization group (td-DMRG) algorithms in the ab initio context, to compute dynamical correlation functions of correlated systems. We…

Chemical Physics · Physics 2017-11-21 Enrico Ronca , Zhendong Li , Carlos A. Jimenez-Hoyos , Garnet Kin-Lic Chan

Dataset Condensation (DC) aims to reduce deep neural networks training efforts by synthesizing a small dataset such that it will be as effective as the original large dataset. Conventionally, DC relies on a costly bi-level optimization…

Computer Vision and Pattern Recognition · Computer Science 2024-12-09 Sahar Rahimi Malakshan , Mohammad Saeed Ebrahimi Saadabadi , Ali Dabouei , Nasser M. Nasrabadi

Deep neural networks (DNNs) have numerous applications across various domains. Several optimization techniques, such as ResNet and SENet, have been proposed to improve model accuracy. These techniques improve the model performance by…

Computer Vision and Pattern Recognition · Computer Science 2024-11-22 Wenxiang Xu , Tian Qiu , Linyun Zhou , Zunlei Feng , Mingli Song , Huiqiong Wang

The recently developed localized orbital scaling correction (LOSC) method shows the ability to systematically and size-consistently reduce the delocalization error existing in conventional density functional approximations (DFAs). Applying…

Chemical Physics · Physics 2020-10-19 Yuncai Mei , Zehua Chen , Weitao Yang

Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte Carlo (FN-DMC) calculations. FN-DMC, being a projection…

Chemical Physics · Physics 2025-09-03 Kousuke Nakano , Benjamin X. Shi , Dario Alfè , Andrea Zen

Distributed point charge models (DCM) and their minimal variants (MDCM) have been integrated with tools widely used for condensed-phase simulations, including a virial-based barostat and a slow-growth algorithm for thermodynamic…

Chemical Physics · Physics 2020-11-03 Mike Devereux , Marco Pezzella , Shampa Raghunathan , Markus Meuwly

We present a correction method for the density of states (DOS) obtained from the generalized ensemble simulations. The DOS is proportionally corrected to match the exact values and/or good approximations known for the system. We demonstrate…

Statistical Mechanics · Physics 2012-05-16 Shijun Lei

In many applications, we collect independent samples from interconnected populations. These population distributions share some latent structure, so it is advantageous to jointly analyze the samples. One effective way to connect the…

Methodology · Statistics 2021-03-08 Archer Gong Zhang , Jiahua Chen

In 3D shape reconstruction based on template mesh deformation, a regularization, such as smoothness energy, is employed to guide the reconstruction into a desirable direction. In this paper, we highlight an often overlooked property in the…

Graphics · Computer Science 2023-08-01 Yucheol Jung , Hyomin Kim , Gyeongha Hwang , Seung-Hwan Baek , Seungyong Lee

We propose a systematic procedure for the approximation of density functionals in density functional theory that consists of two parts. First, for the efficient approximation of a general density functional, we introduce an efficient ansatz…

Strongly Correlated Electrons · Physics 2016-08-29 Michael Lubasch , Johanna I. Fuks , Heiko Appel , Angel Rubio , J. Ignacio Cirac , Mari-Carmen Bañuls

To accommodate the explosive wireless traffics, massive multiple-input multiple-output (MIMO) is regarded as one of the key enabling technologies for next-generation communication systems. In massive MIMO cellular networks, coordinated…

Information Theory · Computer Science 2023-03-27 Jungang Ge , Ying-Chang Liang , Liao Zhang , Ruizhe Long , Sumei Sun

The leading-order dispersion and exchange-dispersion terms in symmetry-adapted perturbation theory (SAPT), $E^{(20)}_{\rm disp}$ and $E^{(20)}_{\rm exch-disp}$, suffer from slow convergence to the complete basis set limit. To alleviate this…

Chemical Physics · Physics 2021-01-26 Monika Kodrycka , Konrad Patkowski

A momentum-space approach of the density-matrix renormalization-group (DMRG) method is developed. Ground state energies of the Hubbard model are evaluated using this method and compared with exact diagonalization as well as quantum…

Condensed Matter · Physics 2009-10-28 T. Xiang

This paper presents the first implementation of a coupling between advanced wave function theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations…

Chemical Physics · Physics 2024-04-12 Maxime Labat , Emmanuel Giner , Guillaume Jeanmairet

Density power divergence (DPD) is designed to robustly estimate the underlying distribution of observations, in the presence of outliers. However, DPD involves an integral of the power of the parametric density models to be estimated; the…

Methodology · Statistics 2024-02-09 Akifumi Okuno
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