Search results for "Statistical physics"

showing 10 items of 1402 documents

The Ornstein-Uhlenbeck Process

2009

PhysicsEconophysicsStochastic processOrnstein–Uhlenbeck processStatistical physics
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Monte Carlo simulation of correlated electrons in disordered systems

1992

Abstract The properties of many-electron states in disordered systems with long-range electron-eletron interaction are investigated by means of a Monte Carlo simulation. Using the Metropolis algorithm, three-dimensional systems up to 512 sites are systematically analysed. The low-lying excitations are investigated in order to distinguish between one-particle and many-particle hopping. In the interesting regime in which disorder and correlation effects are equally important we find that variable-range hopping is insignificant for electron transfer when compared with the contribution from nearest-neighbour one-electron hopping processes as well as variable-number hopping.

PhysicsElectron transferMetropolis–Hastings algorithmCondensed matter physicsGeneral Chemical EngineeringMonte Carlo methodDynamic Monte Carlo methodGeneral Physics and AstronomyStatistical physicsElectronMonte Carlo molecular modelingPhilosophical Magazine B
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Local correlation functional for electrons in two dimensions

2008

We derive a local approximation for the correlation energy in two-dimensional electronic systems. In the derivation we follow the scheme originally developed by Colle and Salvetti for three dimensions, and consider a Gaussian approximation for the pair density. Then, we introduce an ad-hoc modification which better accounts for both the long-range correlation, and the kinetic-energy contribution to the correlation energy. The resulting functional is local, and depends parametrically on the number of electrons in the system. We apply this functional to the homogeneous electron gas and to a set of two-dimensional quantum dots covering a wide range of electron densities and thus various amount…

PhysicsElectronic correlationStrongly Correlated Electrons (cond-mat.str-el)FOS: Physical sciences02 engineering and technologyElectron021001 nanoscience & nanotechnologyCondensed Matter Physics01 natural sciencesElectronic Optical and Magnetic MaterialsRange (mathematics)Condensed Matter - Strongly Correlated ElectronsCorrelation functionQuantum mechanics0103 physical sciencesCorrelation integralDensity functional theoryStatistical physicsLocal-density approximation010306 general physics0210 nano-technologyFermi gas
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CHANGES OF ELECTRONIC NOISE INDUCED BY OSCILLATING FIELDS IN BULK GaAs SEMICONDUCTORS

2008

A Monte Carlo study of hot-electron intrinsic noise in a n-type GaAs bulk driven by one or two mixed cyclostationary electric fields is presented. The noise properties are investigated by computing the spectral density of velocity fluctuations. An analysis of the noise features as a function of the amplitudes and frequencies of two applied fields is presented. Numerical results show that it is possible to reduce the intrinsic noise. The best conditions to realize this effect are discussed.

PhysicsElectronic noiseCyclostationary processGeneral MathematicsMonte Carlo methodQuantum noiseShot noiseField-mixing conditionGeneral Physics and AstronomySpectral densityNoise (electronics)Settore FIS/03 - Fisica Della MateriaComputational physicsElectric fieldFlicker noiseStatistical physicsMonte Carlo simulationFluctuation and Noise Letters
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Calculating thermodynamic properties from fluctuations at small scales.

2011

We show how density and energy fluctuations of small nonperiodic systems embedded in a reservoir can be used to determine macroscopic thermodynamic properties like the enthalpy density and the thermodynamic correction factor. For mixtures, the same formalism leads to a very convenient method to obtain so-called total correlation function integrals, also often referred to as Kirkwood-Buff integrals. Using finite size scaling, the properties obtained for small systems can be extrapolated to the macroscopic system limit provided that the system is sufficiently far from the critical point. As derived in our previous work (Chem. Phys. Lett. 2011, 504, 199-201), the finite size scaling is signifi…

PhysicsEnthalpyMaterials ChemistryThermodynamicsStatistical physicsPhysical and Theoretical ChemistryComputer Science::DatabasesSurfaces Coatings and FilmsThe journal of physical chemistry. B
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Quantum Monte Carlo methods

2005

Introduction In most of the discussion presented so far in this book, the quantum character of atoms and electrons has been ignored. The Ising spin models have been an exception, but since the Ising Hamiltonian is diagonal (in the absence of a transverse magnetic field), all energy eigenvalues are known and the Monte Carlo sampling can be carried out just as in the case of classical statistical mechanics. Furthermore, the physical properties are in accord with the third law of thermodynamics for Ising-type Hamiltonians (e.g. entropy S and specific heat vanish for temperature T → 0, etc.) in contrast to the other truly classical models dealt with in previous chapters (e.g. classical Heisenbe…

PhysicsEntropy (statistical thermodynamics)Quantum Monte CarloMonte Carlo methodZero-point energyClassical fluidsStatistical mechanicsHybrid Monte Carlosymbols.namesakeQuantum mechanicsDynamic Monte Carlo methodsymbolsMonte Carlo method in statistical physicsIsing modelKinetic Monte CarloStatistical physicsQuasi-Monte Carlo methodHamiltonian (quantum mechanics)Monte Carlo molecular modelingSpin-½
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Entropy Driven Phase Separation

2006

PhysicsEntropy drivenStatistical physics
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BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks

2019

Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the NVT ensemble at various densities. We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient and the Carnahan-Starling equation of state for hard sphere liquids. Furthermore, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, g(r), a task that is often performed for inverse design and coarse-graini…

PhysicsEquation of state010304 chemical physicsArtificial neural networkComputer Science::Neural and Evolutionary ComputationFOS: Physical sciencesGeneral Physics and AstronomyInverseDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Soft Condensed MatterCondensed Matter - Disordered Systems and Neural Networks010402 general chemistry01 natural sciences0104 chemical sciencesMolecular dynamicsDistribution functionVirial coefficient0103 physical sciencesVirial expansionSoft Condensed Matter (cond-mat.soft)Statistical physicsPhysical and Theoretical ChemistryPair potentialThe Journal of Chemical Physics
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A generalization of the Carnahan–Starling approach with applications to four- and five-dimensional hard spheres

2018

Abstract Development of good equations of state for hard spheres is an important task in the study of real fluids. In a way consistent with other theoretical results, we generalize the famous Carnahan–Starling approach for arbitrary dimensions and apply it to four- and five-dimensional hard spheres. We obtain simple and integer representations for virial coefficients of lower orders and accurate equations of state. Since theoretically and practically validated, these results improve understanding of hard sphere fluids.

PhysicsEquation of stateGeneralizationGeneral Physics and AstronomyHard spheres01 natural sciences010305 fluids & plasmasVirial coefficientSimple (abstract algebra)0103 physical sciencesDevelopment (differential geometry)Statistical physics010306 general physicsInteger (computer science)Physics Letters A
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Comment on “Scaling behavior in explosive fragmentation”

2002

We discuss the data analysis and the conclusions based upon the analysis given in the paper by Diehl et al. Following the suggestion in the Comment on our previous work by Astrom, Linna, and Timonen [Phys. Rev. E 65,048101 (2002)], we performed extensive molecular-dynamics simulations to confirm that our numerical results for the mass distribution of fragments after the "explosion" of thermalized samples are consistent with the scaling form n(m)∼m - ( α + 1 ) f(m/M 0 ), where ∫(m/M 0 ) is a cutoff function, M 0 is a cutoff parameter, and the exponent a is close to zero.

PhysicsExplosive materialMass distributionFragmentation (mass spectrometry)ExponentCutoffStatistical physicsScalingCutoff functionMathematical physicsPhysical Review E
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