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We present an elementary and self-contained account of the analogies existing between classical diffusion and the imaginary-time evolution of quantum systems. These analogies are used to develop a new quantum simulation method which allows…

Condensed Matter · Physics 2007-05-23 S. Baroni , S. Moroni

This paper deals with the optimization of trial states for the computation of dominant eigenvalues of operators and very large matrices. In addition to preliminary results for the energy spectrum of van der Waals clusters, we review results…

Chemical Physics · Physics 2007-05-23 Vilen Melik-Alaverdian , M. P. Nightingale

We propose a new ensemble for Monte Carlo simulations, in which each state is assigned a statistical weight $1/k$, where $k$ is the number of states with smaller or equal energy. This ensemble has robust ergodicity properties and gives…

Condensed Matter · Physics 2016-08-31 Bobby Hesselbo , R. B. Stinchcombe

Statistical systems on random networks can be formulated in terms of partition functions expressed with integrals by regarding Feynman diagrams as random networks. We consider the cases of random networks with bounded but generic degrees of…

High Energy Physics - Theory · Physics 2014-08-18 Naoki Sasakura , Yuki Sato

We introduce a parallel Wang-Landau method based on the replica-exchange framework for Monte Carlo simulations. To demonstrate its advantages and general applicability for simulations of complex systems, we apply it to different spin models…

Computational Physics · Physics 2013-05-27 Thomas Vogel , Ying Wai Li , Thomas Wüst , David P. Landau

The absolute free energy -- or partition function, equivalently -- of a molecule can be estimated computationally using a suitable reference system. Here, we demonstrate a practical method for staging such calculations by growing a molecule…

Biological Physics · Physics 2009-01-31 Xin Zhang , Artem B. Mamonov , Daniel M. Zuckerman

We present here an extension of the Wang-Landau Monte Carlo method which allows us to get very accurate estimates of the full probability distributions of several observables after a quantum quench for large systems, whenever the relevant…

Statistical Mechanics · Physics 2015-06-09 Simone Ziraldo , Giuseppe E. Santoro

In simulations of crystals, unlike liquids or gases, it may happen that the properties of the studied system depend not only on the volume of the simulation cell but also on its shape. For such cases it is desirable to change the shape of…

Computational Physics · Physics 2022-10-04 A. Baumketner

An efficient Quantum Monte Carlo algorithm for the simulation of bosonic systems on a lattice in a grand canonical ensemble is proposed. It is based on the mapping of bosonic models to the spin models in the limit of the infinite total spin…

Statistical Mechanics · Physics 2007-05-23 Jurij Smakov , Kenji Harada , Naoki Kawashima

We devise automated workflows for the calculation of Helmholtz and Gibbs free energies and their temperature and pressure dependence and provide the corresponding computational tools. We employ non-equilibrium thermodynamics for evaluating…

Materials Science · Physics 2021-10-13 Sarath Menon , Yury Lysogorskiy , Jutta Rogal , Ralf Drautz

The Landau Free Energy determines the landscape of order parameter fluctuations that occur in a physical system at thermal equilibrium and, in particular, characterizes the critical phenomena. We propose a semi-analytical approach based on…

Statistical Mechanics · Physics 2023-12-07 S. Semenov , A. N. Rubtsov

We propose a new method to compute the free energy or enthalpy of fluids or disordered solids by computer simulation . The main idea is to construct a reference system by freezing one representative configuration, and then carry out a…

Computational Physics · Physics 2011-03-16 Friederike Schmid , Tanja Schilling

We construct an effective Hamiltonian via Monte Carlo from a given action. This Hamiltonian describes physics in the low energy regime. We test it by computing spectrum, wave functions and thermodynamical observables (average energy and…

Quantum Physics · Physics 2009-10-31 H. Jirari , H. Kröger , X. Q. Luo , K. J. M. Moriarty

In this work, we introduce a simple modification of the Monte Carlo algorithm, which we call step Monte Carlo (sMC). The sMC approach allows to simulate processes far from equilibrium and obtain information about the dynamic properties of…

Other Condensed Matter · Physics 2023-12-15 Dariusz Sztenkiel

We study Monte Carlo calculations of the effective potential for a scalar field theory using three techniques. One of these is a new method proposed and tested for the first time. In each case we extract the renormalised quantities of the…

High Energy Physics - Lattice · Physics 2010-03-04 A. Ardekani , A. G. Williams

We have developed an efficient Monte Carlo algorithm, which accelerates slow Monte Carlo dynamics in quasi-one-dimensional Ising spin systems. The loop algorithm of the quantum Monte Carlo method is applied to the classical spin models with…

Statistical Mechanics · Physics 2009-11-13 Tota Nakamura

We demonstrate a scaling method for non-Markovian Monte Carlo wave-function simulations used to study open quantum systems weakly coupled to their environments. We derive a scaling equation, from which the result for the expectation values…

Quantum Physics · Physics 2009-11-10 J. Piilo , S. Maniscalco , A. Messina , F. Petruccione

In the last few years we have been developing a Monte Carlo simulation method to cope with systems of many electrons and ions in the Born-Oppenheimer (BO) approximation, the Coupled Electron-Ion Monte Carlo Method (CEIMC). Electronic…

Computational Physics · Physics 2007-05-23 Carlo Pierleoni , David M. Ceperley

We propose an adiabatic reweighting algorithm for computing the free energy along an external parameter from adaptive molecular dynamics simulations. The adaptive bias is estimated using Bayes identity and information from all the sampled…

Statistical Mechanics · Physics 2014-04-08 Lingling Cao , Gabriel Stoltz , Tony Lelièvre , Mihai-Cosmin Marinica , Manuel Athènes

We present two Monte Carlo sampling algorithms for probabilistic inference that guarantee polynomial-time convergence for a larger class of network than current sampling algorithms provide. These new methods are variants of the known…

Artificial Intelligence · Computer Science 2013-02-18 Malcolm Pradhan , Paul Dagum