Search results for " Monte Carlo"

showing 10 items of 400 documents

Group Metropolis Sampling

2017

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…

Computer scienceMonte Carlo methodMarkov processSlice samplingProbability density function02 engineering and technologyMultiple-try MetropolisBayesian inferenceMachine learningcomputer.software_genre01 natural sciencesHybrid Monte Carlo010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0101 mathematicsComputingMilieux_MISCELLANEOUSMarkov chainbusiness.industryRejection samplingSampling (statistics)020206 networking & telecommunicationsMarkov chain Monte CarloMetropolis–Hastings algorithmsymbolsMonte Carlo method in statistical physicsMonte Carlo integrationArtificial intelligencebusinessParticle filter[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerAlgorithmImportance samplingMonte Carlo molecular modeling
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Recycling Gibbs sampling

2017

Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…

Computer scienceMonte Carlo methodSlice samplingMarkov processProbability density function02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesHybrid Monte Carlo010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0101 mathematicsComputingMilieux_MISCELLANEOUSbusiness.industryRejection samplingEstimator020206 networking & telecommunicationsMarkov chain Monte CarlosymbolsArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerAlgorithmGibbs sampling2017 25th European Signal Processing Conference (EUSIPCO)
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Quantum Monte Carlo study of high pressure solid molecular hydrogen

2013

We use the diffusion quantum Monte Carlo (DMC) method to calculate the ground state phase diagram of solid molecular hydrogen and examine the stability of the most important insulating phases relative to metallic crystalline molecular hydrogen. We develop a new method to account for finite-size errors by combining the use of twist-averaged boundary conditions with corrections obtained using the Kwee-Zhang-Krakauer (KZK) functional in density functional theory. To study band-gap closure and find the metallization pressure, we perform accurate quasi-particle many-body calculations using the $GW$ method. In the static approximation, our DMC simulations indicate a transition from the insulating…

Condensed Matter - Materials Science540 Chemistry and allied sciencesMaterials scienceCondensed matter physicsBand gapQuantum Monte CarloClose-packing of equal spheresMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciencesGeneral Physics and Astronomy540 ChemieDensity functional theoryBoundary value problemDiffusion (business)Ground statePhase diagram
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Evidence of nickel ions dimerization in NiWO$_4$ and NiWO$_4$-ZnWO$_4$ solid solutions probed by EXAFS spectroscopy and reverse Monte Carlo simulatio…

2021

G.B. acknowledges the financial support provided by the State Education Development Agency for project No.1.1.1.2/VIAA/3/19/444 (agreement No. 1.1.1.2/16/I/001) realized at the Institute of Solid State Physics, University of Latvia. A.K. and A.K. would like to thank the support of the Latvian Council of Science project No. lzp-2019/1-0071. Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART2.

Condensed Matter - Materials ScienceEXAFSNiWO4solid solutions:NATURAL SCIENCES:Physics [Research Subject Categories]Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesZnWO4antiferromagnetsreverse Monte Carlo
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Peculiarities of the local structure in new medium- and high-entropy, low-symmetry tungstates

2022

G. Bakradze acknowledges financial support provided by the Latvian Council of Science for project no. 1.1.1.2/VIAA/3/19/444 (agreement no. 1.1.1.2/16/I/001) realized at the Institute of Solid State Physics, University of Latvia. The Institute of Solid State Physics, University of Latvia, as a centre of excellence, has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement no. 739508, project CAMART2.

Condensed Matter - Materials ScienceHigh-entropy oxidesMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciences:NATURAL SCIENCES::Physics [Research Subject Categories]TungstatesGeneral ChemistryCondensed Matter Physics540ddc:540Reverse Monte Carlo methodGeneral Materials ScienceSolid solutionsExtended X-ray absorption fine structure
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Multiscale modelling of structure formation of C$_{60}$ on insulating CaF$_2$ substrates

2021

Morphologies of adsorbed molecular films are of interest in a wide range of applications. To study the epitaxial growth of these systems in computer simulations requires access to long time and length scales, and one typically resorts to kinetic Monte Carlo (KMC) simulations. However, KMC simulations require as input transition rates and their dependence on external parameters (such as temperature). Experimental data allow only limited and indirect access to these rates, and models are often oversimplified. Here, we follow a bottom-up approach and aim at systematically constructing all relevant rates for an example system that has shown interesting properties in experiments, buckminsterfull…

Condensed Matter - Materials ScienceStructure formationMaterials science010304 chemical physicsGeneral Physics and AstronomyMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciencesSubstrate (electronics)Computational Physics (physics.comp-ph)010402 general chemistry01 natural sciencesMultiscale modeling0104 chemical sciencesMolecular dynamicschemistry.chemical_compoundCondensed Matter::Materials ScienceBuckminsterfullerenechemistry0103 physical sciencesMolecular filmKinetic Monte CarloStatistical physicsPhysical and Theoretical ChemistryPhysics - Computational PhysicsFree parameter
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In Situ Study of Zinc Peroxide Decomposition to Zinc Oxide by X‐Ray Absorption Spectroscopy and Reverse Monte Carlo Simulations

2022

The authors wish to thank Dr. R. Kalendarev for the synthesis of ZnO2 sample. A.K. would like to thank the financial support of the ERDF Project No. 1.1.1.1/20/A/060. The experiment at the MAX IV synchrotron was performed within the project 20190823. Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union's Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART2.

Condensed Matter - Materials Sciencereverse Monte Carlo methodX-ray absorption spectroscopyMaterials Science (cond-mat.mtrl-sci)FOS: Physical sciences:NATURAL SCIENCES::Physics [Research Subject Categories]Condensed Matter PhysicsElectronic Optical and Magnetic MaterialsEXAFSCondensed Matter::Materials Sciencephase transitionZnOPhysics::Atomic and Molecular ClustersZnO2physica status solidi (b)
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Monte Carlo Simulations of Alloy Phase Transformations

1994

The use of Monte Carlo simulation methods for study of order-disorder phase transitions in lattice models of alloys is reviewed, with an emphasis on interfacial phenomena and the kinetics of ordering and/or phase separation. Topics discussed include the attempt to predict the phase diagram of Fe-Al alloys from recent measurements of effective interaction parameters, competition between magnetic and crystallographic ordering in such alloys, and the structure of their antiphase domain boundaries. Both an interfacial roughening transition of this domain wall and interfacial enrichment phenomena are predicted. Then simulations of alloy-vacuum surfaces are discussed, and it is shown that both ca…

Condensed Matter::Materials SciencePhase transitionMaterials scienceCondensed matter physicsSpinodal decompositionPhase (matter)Monte Carlo methodDynamic Monte Carlo methodMonte Carlo method in statistical physicsKinetic Monte CarloMonte Carlo molecular modeling
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Realistic investigations of correlated electron systems with LDA + DMFT

2006

Conventional band structure calculations in the local density approximation (LDA) [1–3] are highly successful for many materials, but miss important aspects of the physics and energetics of strongly correlated electron systems, such as transition metal oxides and f-electron systems displaying, e.g., Mott insulating and heavy quasiparticle behavior. In this respect, the LDA + DMFT approach which merges LDA with a modern many-body approach, the dynamical mean-field theory (DMFT), has proved to be a breakthrough for the realistic modeling of correlated materials. Depending on the strength of the electronic correlation, a LDA + DMFT calculation yields the weakly correlated LDA results, a strong…

Condensed Matter::Quantum GasesCondensed matter physicsHubbard modelElectronic correlationChemistryMott insulatorQuantum Monte CarloCondensed Matter PhysicsElectronic Optical and Magnetic MaterialsQuasiparticleCondensed Matter::Strongly Correlated ElectronsStrongly correlated materialddc:530Metal–insulator transitionLocal-density approximation
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Quasi-continuous-time impurity solver for the dynamical mean-field theory with linear scaling in the inverse temperature

2013

We present an algorithm for solving the self-consistency equations of the dynamical mean-field theory (DMFT) with high precision and efficiency at low temperatures. In each DMFT iteration, the impurity problem is mapped to an auxiliary Hamiltonian, for which the Green function is computed by combining determinantal quantum Monte Carlo (BSS-QMC) calculations with a multigrid extrapolation procedure. The method is numerically exact, i.e., yields results which are free of significant Trotter errors, but retains the BSS advantage, compared to direct QMC impurity solvers, of linear (instead of cubic) scaling with the inverse temperature. The new algorithm is applied to the half-filled Hubbard mo…

Condensed Matter::Quantum GasesModels StatisticalStrongly Correlated Electrons (cond-mat.str-el)Hubbard modelQuantum Monte CarloTemperatureExtrapolationFOS: Physical sciencesMott transitionCondensed Matter - Strongly Correlated Electronssymbols.namesakeMultigrid methodQuantum mechanicsLinear ModelssymbolsLinear scaleThermodynamicsComputer SimulationCondensed Matter::Strongly Correlated ElectronsStatistical physicsHamiltonian (quantum mechanics)ScalingAlgorithmsMathematicsPhysical Review E
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