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We introduce an approach to improve single-reference coupled cluster theory in settings where the Aufbau determinant is absent from or plays only a small role in the true wave function. Using a de-excitation operator that can be efficiently…

Chemical Physics · Physics 2024-07-03 Harrison Tuckman , Eric Neuscamman

In this contribution, we extend our framework for analyzing and visualizing correlated many-electron dynamics to non-variational, highly scalable electronic structure method. Specifically, an explicitly time-dependent electronic wave packet…

Chemical Physics · Physics 2017-04-27 Gunter Hermann , Vincent Pohl , Jean Christophe Tremblay

We develop a linked cluster method to calculate the spectral weights of many-particle excitations at zero temperature. The dynamical structure factor is expressed as a sum of exclusive structure factors, each representing contributions from…

Strongly Correlated Electrons · Physics 2009-11-10 Chris J. Hamer , Weihong Zheng , Rajiv R. P. Singh

This paper proposes a higher-order multiscale computational method for nonlinear thermo-electric coupling problems of composite structures, which possess temperature-dependent material properties and nonlinear Joule heating. The innovative…

Numerical Analysis · Mathematics 2025-01-24 Hao Dong , Zongze Yang , Yufeng Nie

We investigate the accuracy of a number of wavefunction based methods at the heart of quantum chemistry for metallic systems. Using Hartree-Fock as a reference, perturbative (M{\o}ller-Plesset, MP) and coupled cluster (CC) theories are used…

Chemical Physics · Physics 2013-10-24 James J. Shepherd , Andreas Grüneis

Linear response time-dependent density functional theory is used to study low-lying electronic continuum states of targets that can bind an extra electron. Exact formulas to extract scattering amplitudes from the susceptibility are derived…

Other Condensed Matter · Physics 2009-11-11 Adam Wasserman , Neepa T. Maitra , Kieron Burke

We review and further develop the recently introduced numerical approach for scattering calculations based on a so called pseudo-time Schroedinger equation, which is in turn a modification of the damped Chebyshev polynomial expansion…

Chemical Physics · Physics 2007-05-23 V. A. Mandelshtam , A. Neumaier

We present the first successful application of the coupled cluster approach to simulate the X-ray absorption (XA) spectrum of liquid water. The system size limitations of standard coupled cluster theory are overcome by employing a newly…

To examine the applicability of the time-dependent density-functional theory (TDDFT) for treating the electron-nucleus coupling in excited states, we calculate the strength distribution associated with the pi-pi* transition in ethylene. The…

Chemical Physics · Physics 2007-05-23 G. F. Bertsch , J. Giansiracusa , K. Yabana

Time-resolved spectroscopy provides the main tool for analyzing the dynamics of excitonic energy transfer in light-harvesting complexes. To infer time-scales and effective coupling parameters from experimental data requires to develop…

Chemical Physics · Physics 2018-11-28 Tobias Kramer , Mirta Rodriguez , Yaroslav Zelinskyy

Multicomponent systems are defined as chemical systems that require a quantum mechanical description of two or more different types of particles. Non-Born-Oppenheimer electron-nuclear interactions in molecules, electron-hole interactions in…

Chemical Physics · Physics 2015-10-21 Benjamin H. Ellis , Somil Aggarwal , Arindam Chakraborty

For autonomous systems it is well known how to extract tunneling probabilities from wavepacket calculations. Here we present a corresponding approach for periodically time-dependent Hamiltonians, valid at all frequencies, field strengths,…

Mesoscale and Nanoscale Physics · Physics 2009-11-10 Frank Grossmann

Background: Applications of nuclear time-dependent density functional theory (TDDFT) are often capable of providing quantitative description of heavy ion reactions. However, the structure of pre-compound states produced in heavy ion…

Nuclear Theory · Physics 2017-12-20 Bastian Schuetrumpf , Witold Nazarewicz

This study addresses the problem of convolutional kernel learning in univariate, multivariate, and multidimensional time series data, which is crucial for interpreting temporal patterns in time series and supporting downstream machine…

Machine Learning · Computer Science 2025-04-17 Xinyu Chen , HanQin Cai , Fuqiang Liu , Jinhua Zhao

Background: The complex scaling method (CSM) has been successfully used to describe many-body resonances as eigenvalues of the complex-scaled Hamiltonian in an appropriate $L^2$ basis representation. Its scope has subsequently been extended…

Nuclear Theory · Physics 2026-04-03 Yuma Kikuchi , Kiyoshi Katō , Takayuki Myo

Causality implies that by measuring an absorption spectrum, the time-dependent linear response function can be retrieved. Recent experiments suggest a link between the shape of spectral lines observed in absorption spectroscopy with the…

The Coupled-Cluster theory is one of the most successful high precision methods used to solve the stationary Schr\"odinger equation. In this article, we address the mathematical foundation of this theory with focus on the advances made in…

Chemical Physics · Physics 2019-09-04 Andre Laestadius , Fabian M. Faulstich

The computation of the nuclear quantum dynamics of molecules is challenging, requiring both accuracy and efficiency to be applicable to systems of interest. Recently, theories have been developed for employing time-dependent basis functions…

A recent mode coupling theory of higher-order correlation functions is tested on a simple hard-sphere fluid system at intermediate densities. Multi-point and multi-time correlation functions of the densities of conserved variables are…

Statistical Mechanics · Physics 2009-11-07 Ramses van Zon , Jeremy Schofield

Multivariate Time-Series (MTS) clustering is crucial for signal processing and data analysis. Although deep learning approaches, particularly those leveraging Contrastive Learning (CL), are prominent for MTS representation, existing…

Machine Learning · Computer Science 2026-01-13 Zexi Tan , Tao Xie , Haoyi Xiao , Baoyao Yang , Yuzhu Ji , An Zeng , Xiang Zhang , Yiqun Zhang