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Detecting and controlling nuclear spin nano-ensembles is crucial for the further development of nuclear magnetic resonance (NMR) spectroscopy and for the emerging solid state quantum technology. Here we present the fabrication of a…

Recently, many experiments with cold atomic gases have been conducted from interest in the non-equilibrium dynamics of correlated quantum systems. Of these experiments, the mixing dynamics of fermion clusters motivates us to research…

Quantum Gases · Physics 2014-07-31 Jun'ichi Ozaki , Masaki Tezuka , Norio Kawakami

The nuclear spins in nanostructured semiconductors play a central role in quantum applications. The nuclear spins represent a useful resource for generating local magnetic fields but nuclear spin noise represents a major source of dephasing…

We discuss recently introduced numerical linked-cluster (NLC) algorithms that allow one to obtain temperature-dependent properties of quantum lattice models, in the thermodynamic limit, from exact diagonalization of finite clusters. We…

Statistical Mechanics · Physics 2007-06-25 Marcos Rigol , Tyler Bryant , Rajiv R. P. Singh

We employ a classical limit grounded in SU(4) coherent states to investigate the temperature-dependent dynamical spin structure factor of the $S = 1/2$ ladder consisting of weakly coupled dimers. By comparing the outcomes of this classical…

Strongly Correlated Electrons · Physics 2024-12-25 David A. Dahlbom , Jinu Thomas , Steven Johnston , Kipton Barros , Cristian D. Batista

In this paper, a hybrid quasi-static atomistic simulation method at finite temperature is developed, which combines the advantages of MD for thermal equilibrium and atomic-scale finite element method (AFEM) for efficient equilibration. Some…

Atomic Physics · Physics 2015-06-17 Ran Xu , Bin Liu

Nuclear magnetic resonance (NMR) is a powerful tool for applications ranging from chemical analysis to quantum information processing. Achieving optical initialization and detection of molecular nuclear spins promises new opportunities -…

The accurate prediction of temperature-dependent magnetization dynamics is a fundamental challenge in computational magnetism. While Atomistic Spin Dynamics (ASD) simulations have emerged as a powerful tool for studying magnetic phenomena,…

Materials Science · Physics 2025-08-18 Fried-Conrad Weber , Felix Hartmann , Matias Bargheer , Janet Anders , Richard F. L. Evans

We simulate the dynamical spin structure factor (DSSF) $\mathcal{S}({q},\omega)$ of the spin-1/2 Heisenberg antiferromagnetic chain using classical simulations. By employing Landau-Lifshitz Dynamics, we emulate quantum correlations through…

Strongly Correlated Electrons · Physics 2026-01-15 Chaebin Kim , Martin Mourigal

Equilibration of observables in closed quantum systems that are described by a unitary time evolution is a meanwhile well-established phenomenon apart from a few equally well-established exceptions. Here we report the surprising theoretical…

Strongly Correlated Electrons · Physics 2021-09-22 P. Vorndamme , H. -J. Schmidt , Chr. Schröder , J. Schnack

We investigate analytically and numerically the Multiple Quantum (MQ) NMR dynamics in dipolar ordered spin systems of nuclear spins 1/2 at a low temperatures. We suggest two different methods of MQ NMR. One of them is based on the…

Mesoscale and Nanoscale Physics · Physics 2013-09-24 G. B. Furman , V. M. Meerovich , V. L. Sokolovsky

We study the correlation functions of quantum spin $1/2$ ladders at finite temperature, under a magnetic field, in the gapless phase at various relevant temperatures $T\neq 0$, momentum $q$ and frequencies $\omega$. We compute those…

Strongly Correlated Electrons · Physics 2019-05-15 N. Kestin , T. Giamarchi

Nuclear spins and paramagnetic centers in a solid randomly group to form clusters featuring nearly-degenerate, hybrid states whose dynamics are central to processes involving nuclear spin-lattice relaxation and diffusion. Their…

We present an accelerated ``on-the-fly'' coupled-cluster path-integral molecular dynamics (PIMD) method for finite-temperature simulations in which electron correlation and nuclear quantum effects are treated simultaneously. The approach is…

Chemical Physics · Physics 2026-05-20 Thomas Spura , Hossam Elgabarty , Thomas D. Kühne

Inelastic neutron-scattering and finite-temperature density matrix renormalization group (DMRG) calculations are used to investigate the spin excitation spectrum of the $S=1/2$ Heisenberg spin chain compound K$_2$CuSO$_4$Cl$_2$ at several…

Strongly Correlated Electrons · Physics 2017-10-04 Dominic Blosser , Noam Kestin , Kirill Yu. Povarov , Robert Bewley , Emanuele Coira , Thierry Giamarchi , Andrey Zheludev

In recent years, a method for computing spin dynamics at infinite temperature (spinDMFT) was developed. It utilizes the ideas of dynamical mean-field theory for fermions: single-site approximation and a self-consistency condition to…

Strongly Correlated Electrons · Physics 2026-04-24 Przemysław Bieniek , Timo Gräßer , Götz S. Uhrig

State-of-the-art algorithms for simulating fermions coupled to gauge fields often rely on integrating fermion degrees of freedom. While successful in simulating QCD at zero chemical potential, at finite density these approaches are hindered…

High Energy Physics - Lattice · Physics 2024-02-05 Joao C. Pinto Barros , Thea Budde , Marina Krstic Marinkovic

Atomic-scale modeling of magnetic materials requires precise treatment of coupled spin-lattice degrees of freedom (DOFs). Traditional spin-lattice dynamics (SLD), employing Newtonian equation for lattice evolution and the…

Materials Science · Physics 2025-11-06 Zhengtao Huang , Han Wang , Ben Xu

Quasi-equilibrium states that can be prepared in solids through Nuclear Magnetic Resonance (NMR) techniques are out-of-equilibrium states that slowly relax towards thermodynamic equilibrium with the lattice. In this work, we use the quantum…

Quantum Physics · Physics 2023-04-03 J. A. Taboada , H. H. Segnorile , C. E. González , R. C. Zamar

We propose a neural physics system for real-time, interactive fluid simulations. Traditional physics-based methods, while accurate, are computationally intensive and suffer from latency issues. Recent machine-learning methods reduce…

Machine Learning · Computer Science 2025-05-27 Jingxuan Xu , Hong Huang , Chuhang Zou , Manolis Savva , Yunchao Wei , Wuyang Chen