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We describe a modification of the stochastic coupled cluster algorithm that allows the use of multiple reference determinants. By considering the secondary references as excitations of the primary reference and using them to change the…

Chemical Physics · Physics 2020-10-22 Maria-Andreea Filip , Charles J. C. Scott , Alex J. W. Thom

The present paper introduces a new multi-reference perturbation approach developed at second order, based on a Jeziorsky-Mokhorst expansion using individual Slater determinants as perturbers. Thanks to this choice of perturbers, an…

Chemical Physics · Physics 2017-06-28 Emmanuel Giner , Celestino Angeli , Yann Garniron , Anthony Scemama , Jean-Paul Malrieu

We propose a multireference linearized coupled cluster theory using matrix product states (MPS-LCC) which provides remarkably accurate ground-state energies, at a computational cost that has the same scaling as multireference configuration…

Chemical Physics · Physics 2015-09-02 Sandeep Sharma , Ali Alavi

The tailored coupled cluster (TCC) approach is a promising ansatz that preserves the simplicity of single-reference coupled cluster theory, while incorporating a multi-reference wave function through amplitudes obtained from a preceding…

Chemical Physics · Physics 2021-01-07 Maximilian Mörchen , Leon Freitag , Markus Reiher

We present two efficient and intruder-free methods for treating dynamic correlation on top of general multi-configuration reference wave functions---including such as obtained by the density matrix renormalization group (DMRG) with large…

Chemical Physics · Physics 2016-09-13 Sandeep Sharma , Gerald Knizia , Sheng Guo , Ali Alavi

The multi-reference Coupled Cluster method first proposed by Meller et al (J. Chem. Phys. 1996) has been implemented and tested. Guess values of the amplitudes of the single and double excitations (the ${\hat T}$ operator) on the top of the…

Chemical Physics · Physics 2016-04-11 Emmanuel Giner , Grégoire David , Anthony Scemama , Jean Paul Malrieu

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…

In this article we consider Bayesian parameter inference associated to partially-observed stochastic processes that start from a set B0 and are stopped or killed at the first hitting time of a known set A. Such processes occur naturally…

Computation · Statistics 2012-01-19 Ajay Jasra , Nikolas Kantas

We present second-order molecular cluster perturbation theory (MCPT(2)), a linear scaling methodology to calculate arbitrarily large systems with explicit calculation of individual wavefunctions in a coupled-cluster framework. This new…

High-order perturbative $\textit{ab initio}$ calculations are challenging due to the rapidly growing configuration space and the difficulty of assessing convergence. In this letter, we introduce perturbation theory quantum Monte Carlo…

Nuclear Theory · Physics 2026-05-06 Xin Zhen , Rongzhe Hu , Junchen Pei , Furong Xu

A second-order many-body perturbation correction to the relativistic Dirac-Hartree-Fock energy is evaluated stochastically by integrating 13-dimensional products of four-component spinors and Coulomb potentials. The integration in the real…

Quantum Physics · Physics 2022-06-16 J. César Cruz , Jorge Garza , Takeshi Yanai , So Hirata

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

A size-extensive, converging, black-box, ab initio coupled-cluster ($\Delta$CC) ansatz is introduced that computes the energies and wave functions of stationary states from any degenerate or nondegenerate Slater-determinant references with…

Chemical Physics · Physics 2026-05-15 So Hirata

Clustering is fundamental for gaining insights from complex networks, and spectral clustering (SC) is a popular approach. Conventional SC focuses on second-order structures (e.g., edges connecting two nodes) without direct consideration of…

Machine Learning · Computer Science 2018-12-27 Yan Ge , Haiping Lu , Pan Peng

We recently proposed a novel approach to converging electronic energies equivalent to high-level coupled-cluster (CC) computations by combining the deterministic CC($P$;$Q$) formalism with the stochastic configuration interaction (CI) and…

Chemical Physics · Physics 2021-03-23 J. Emiliano Deustua , Jun Shen , Piotr Piecuch

In this paper, we present a methodology to estimate the parameters of stochastically contaminated models under two contamination regimes. In both regimes, we assume that the original process is a variable length Markov chain that is…

Methodology · Statistics 2017-02-23 Denise Duarte , Sokol Ndreca , Wecsley O. Prates

A stochastic subgrid-scale parameterization based on the Ruelle's response theory and proposed in Wouters and Lucarini [2012] is tested in the context of a low-order coupled ocean-atmosphere model for which a part of the atmospheric modes…

Atmospheric and Oceanic Physics · Physics 2017-01-18 Jonathan Demaeyer , Stéphane Vannitsem

In this work, we investigate the possibility of improving multireference-driven coupled cluster (CC) approaches with an algorithm that iteratively combines complete active space (CAS) calculations with tailored CC and externally corrected…

Chemical Physics · Physics 2025-03-06 Robin Feldmann , Max Mörchen , Jakub Lang , Michał Lesiuk , Markus Reiher

The development of multireference coupled cluster (MRCC) techniques has remained an open area of study in electronic structure theory for decades due to the inherent complexity of expressing a multi-configurational wavefunction in the…

Chemical Physics · Physics 2023-05-24 Maria-Andreea Filip , Alex J. W. Thom

Modern neural recording techniques allow neuroscientists to obtain spiking activity of multiple neurons from different brain regions over long time periods, which requires new statistical methods to be developed for understanding structure…

Applications · Statistics 2023-12-29 Ganchao Wei
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