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Arguably the most widely used approaches for obtaining highly accurate molecular ground-state energies are coupled cluster methods. Despite introducing two layers of approximation, a linear and a nonlinear one, coupled cluster methods…

Numerical Analysis · Mathematics 2026-05-22 Jonas Beck , Benjamin Stamm

Quantum embedding methods have recently developed significantly to model large molecular structures. This work proposes a novel wave function theory in density functional theory (WTF-in-DFT) embedding scheme based on pair-coupled cluster…

Chemical Physics · Physics 2024-01-10 Rahul Chakraborty , Katharina Boguslawski , Paweł Tecmer

We reveal limitations of several standard coupled-cluster (CC) methods with perturbation-theorybased noniterative or approximate iterative treatments of triple excitations when applied to thedetermination of highly accurate potential energy…

Chemical Physics · Physics 2021-10-04 Jan Schnabel , Lan Cheng , Andreas Köhn

Correlation clustering is a widely studied framework for clustering based on pairwise similarity and dissimilarity scores, but its best approximation algorithms rely on impractical linear programming relaxations. We present faster…

Data Structures and Algorithms · Computer Science 2022-06-27 Nate Veldt

Introducing an active space approximation is inevitable for the quantum computations of chemical systems. However, this approximation ignores the electron correlations related to non-active orbitals. Here, we propose a computational method…

Quantum Physics · Physics 2024-06-06 Luca Erhart , Yuichiro Yoshida , Viktor Khinevich , Wataru Mizukami

The central problem in electronic structure theory is the computation of the eigenvalues of the electronic Hamiltonian -- an unbounded, self-adjoint operator acting on a Hilbert space of antisymmetric functions. Coupled cluster (CC)…

Numerical Analysis · Mathematics 2023-01-30 Muhammad Hassan , Yvon Maday , Yipeng Wang

We investigate the role of the electron correlation effects in the calculations of the electric dipole polarizabilities (\alpha) of elements belonging to three different groups of periodic table. To understand the propagation of the…

Atomic Physics · Physics 2014-05-21 Yashpal Singh , B. K. Sahoo

Coupled cluster theory is a vital cornerstone of electronic structure theory and is being applied to ever-larger systems. Stochastic approaches to quantum chemistry have grown in importance and offer compelling advantages over traditional…

We investigate the use of orbital-optimized references in conjunction with single-reference coupled-cluster theory with single and double substitutions (CCSD) for the study of core excitations and ionizations of 18 small organic molecules,…

Theory of one-electron transition density matrices has been formulated within the time-independent coupled cluster method for the polarization propagator [R. Moszynski, P. S. \.Zuchowski, and B. Jeziorski, Coll. Czech. Chem. Commun. {\bf…

Chemical Physics · Physics 2014-09-25 Aleksandra M. Tucholska , Marcin Modrzejewski , Robert Moszynski

We present a new and efficient implementation of the closed shell coupled cluster singles and doubles with perturbative triples method (CC3) in the electronic structure program $e^T$. Asymptotically, a ground state calculation has an…

Chemical Physics · Physics 2020-10-29 Alexander C. Paul , Rolf H. Myhre , Henrik Koch

An efficiency of the Tucker decomposition of amplitude tensors within the single-reference relativistic coupled cluster method with single and double excitations (RCCSD) was studied in a series of benchmark calculations for (AuCl)$_n$…

Computational Physics · Physics 2025-07-29 Alexander V. Oleynichenko , Artem S. Rumiantsev , Andrei Zaitsevskii , Ephraim Eliav

In the Correlation Clustering problem we are given $n$ nodes, and a preference for each pair of nodes indicating whether we prefer the two endpoints to be in the same cluster or not. The output is a clustering inducing the minimum number of…

Data Structures and Algorithms · Computer Science 2025-01-07 Nick Fischer , Evangelos Kipouridis , Jonas Klausen , Mikkel Thorup

Correlation Clustering is a classic clustering objective arising in numerous machine learning and data mining applications. Given a graph $G=(V,E)$, the goal is to partition the vertex set into clusters so as to minimize the number of edges…

Data Structures and Algorithms · Computer Science 2024-07-17 Vincent Cohen-Addad , David Rasmussen Lolck , Marcin Pilipczuk , Mikkel Thorup , Shuyi Yan , Hanwen Zhang

Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and…

Chemical Physics · Physics 2019-12-10 Christoph Schran , Jörg Behler , Dominik Marx

Basis set convergence of correlation effects on molecular atomization energies beyond the CCSD (coupled cluster with singles and doubles) approximation has been studied near the one-particle basis set limit. Quasiperturbative connected…

Chemical Physics · Physics 2008-08-17 Amir Karton , Peter R. Taylor , Jan M. L. Martin

Electronic structure methods built around double-electron excitations have a rich history in quantum chemistry. However, it seems to be the case that such methods are only suitable in particular situations and are not naturally equipped to…

Chemical Physics · Physics 2024-07-12 Zachary W. Windom , Ajith Perera , Rodney J. Bartlett

The unitary coupled cluster (UCC) approximation is one of the more promising wave-function ans\"atze for electronic structure calculations on quantum computers via the variational quantum eigensolver algorithm. However, for large systems…

Quantum Physics · Physics 2021-09-30 Jia Chen , Hai-Ping Cheng , James K. Freericks

Clustering with capacity constraints is a fundamental problem that attracted significant attention throughout the years. In this paper, we give the first FPT constant-factor approximation algorithm for the problem of clustering points in a…

Data Structures and Algorithms · Computer Science 2024-02-21 Sayan Bandyapadhyay , William Lochet , Saket Saurabh

Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining. In correlation clustering, one receives as input a signed graph and the goal is to partition it to minimize the number of…

Data Structures and Algorithms · Computer Science 2021-06-17 Vincent Cohen-Addad , Silvio Lattanzi , Slobodan Mitrović , Ashkan Norouzi-Fard , Nikos Parotsidis , Jakub Tarnawski