Top PDF Development and Applications of Quantum Monte Carlo

Development and Applications of Quantum Monte Carlo

Development and Applications of Quantum Monte Carlo

Variational and di usion Monte Carlo, the most ommonly used ele troni stru ture QMC variants, use sto hasti methods to optimize wavefun tions and al ulate expe tation values [56℄ and an [r]

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Applications of the Variational Quantum Monte Carlo Method to the Two-Electron Atoms

Applications of the Variational Quantum Monte Carlo Method to the Two-Electron Atoms

In Table 5 we present the results obtained for the ground state of the confined helium atom ( = 2 ) together with the corresponding results available in previous works. The obtained energies calculated for a wide range of values of 5 . In Table 6 we present the energies of the ground state of the confined Li + as functions of the spherical box radius. Finally, in Table 7 we present the resulting energies of the ground state of the confined Be 2+ as functions of the spherical box radius. It seen from these tables that the small values of the spherical box radius 5 describe the case of strong confinement where for large values, 5 ≥ 3.5 , the compression effect becomes not noticeable and the energy is nearly stable and approaches to the corresponding exact value. Our results are in good agreement, in comparison, with previous data. As the atoms are compressed, they become constrained in a diminishing spherical box so that according to the quantum mechanical uncertainty principle, the electrons increase their momentum and thereby leading to a net gathering of kinetic energy. When the increase in the confinement kinetic energy becomes predominant and cannot be compensated by the increase of the Coulomb attractive energy, the energies of the confined helium atom increase. Table 5. Energies of the ground state of the confined helium atom as functions of the spherical box radius. All values are in atomic units.
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Quantum Monte Carlo for Transition Metal Systems: Method Developments and Applications

Quantum Monte Carlo for Transition Metal Systems: Method Developments and Applications

With the Schr¨ odinger equation, the rules of non-relativistic quantum mechanics, we can describe almost all of condensed matter. We are confident that these rules, if solved appropriately, can predict the behavior of materials, from what configurations atoms will be found to the hardness of a material to what color and the optical behavior of some object. We have a universal equation for all these things–the only problem is that it is extremely difficult to solve for many interacting particles: the well-known many-body problem. This is worse than the many-body problem for classical systems, like planetary motion, because there does not exist a numerically exact, polynomially scaling method that solves the Schr¨ odinger equation. So we are left performing some kind of approximations, which necessarily cause a loss of universality.
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Chiral 2N and 3N interactions and quantum Monte Carlo applications

Chiral 2N and 3N interactions and quantum Monte Carlo applications

While the rest of the nuclear many-body community adopted chiral EFT interactions as in- put in their calculations, Quantum Monte Carlo methods (namely Green’s function Monte Carlo (GFMC) [15–17] and Auxiliary-Field Diffusion Monte carlo (AFDMC) [18]), did not. Given the accuracy and precision of QMC calculations for strongly interacting systems [19, 20], this state of affairs was problematic, a direct consequence of chiral EFT potentials being non-local. (It’s worth noting that Monte Carlo methods have, however, been used to study neutron matter based on lattice techniques [21] and with momentum-space QMC approaches [22, 23].) The main reason for chiral EFT interactions being non-local was that they are naturally formulated in momentum space, so they were historically constructed without considering their locality or non-locality.
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Applications of the Fixed-node Quantum Monte Carlo Method.

Applications of the Fixed-node Quantum Monte Carlo Method.

Diffusion quantum Monte Carlo (DMC) is a projector quantum Monte Carlo (QMC) method for solving quantum many-body prob- lems. Its name originates from the formal similarity between the Schrödinger’s equation in imaginary time and a classical diffusion equation. Based on stochastic sampling and evolution of particle configurations, DMC projects out the ground state of a given sym- metry from any trial wave function with nonzero overlap. In deal- ing with fermions, DMC has to adopt some strategy for overcoming the infamous fermion sign problem which would make it dramat- ically inefficient for many-particle systems. It is common to cir- cumvent the sign problem by forcing the ground state nodes (zero-locus) to be the same as the nodes of the best available trial function. This nodal constraint ensures that the product of the ground state and the trial wave function becomes nonnegative throughout the configuration space. This is known as the fixed- node (FN) approximation [1–3].
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A Study of the Errors of the Fixed-Node Approximation in Diffusion Monte Carlo.

A Study of the Errors of the Fixed-Node Approximation in Diffusion Monte Carlo.

Quantum Monte Carlo (QMC) methods have proved to be very useful for studies of many-body quantum systems. For example, accurate calculations of paradigmatic models such as homogeneous electron gas [1] and unitary fermions [92] have become important references for other methods. Similarly, QMC applications to electronic structure of many types of systems, from atoms to solids [25] [46], have offered an important alternative to the mainstream approaches such as density functional or basis set correlated methods. The QMC calculations typically employ the fixed-node approximation to deal with the well-known fermion sign problem. In addition, in electronic structure problems it is often necessary to eliminate the atomic cores using appropriate effective core potentials (pseudopotentials). This enables one to study the energy differences which are of the order of ≈ 0.1 eV or larger such as cohesion, optical gaps, equations of state, etc [25] [46].
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Multilevel and quasi Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

Multilevel and quasi Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

In this paper, we investigate three existing methods for outperforming MC, namely, multilevel Monte Carlo (MLMC) [3], quasi-Monte Carlo (QMC) [4] and multilevel quasi-Monte Carlo (MLQMC) [5]. We apply these methodologies to the problem of travel time estimation in heterogeneous porous media. This is of central importance in a series of engineering applications ranging from groundwater management to groundwater remediation. It also involves the development of mathematical models for reactive transport in porous media. These models are used to assess, for instance, groundwater contamination, CO 2 sequestration, residence time distributions, etc. The QoI considered in this study will be the result
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The quantum Monte Carlo method : application to problems in statistical physics

The quantum Monte Carlo method : application to problems in statistical physics

Diffusion Monte Carlo methods have been used mainly in studying electronic systems where the particle interactions vary relatively slowly with distance. Thus the finite time step approximation is expected to be valid. The most significant problem in using quantum Monte Carlo methods to study electronic systems is the treatment of identical particle statistics (Kalos (1984)). In most of the electronic applications of the zero temperature quantum Monte Carlo methods, approximate information about nodal surfaces in the Fermion wave function is used to provide boundary conditions for the random walks. Wave functions obtained from variational calculations are often used for this purpose. With the fixed node approximate methods (Reynolds et al. (1982)) the random walk results are dependent on the nodal surfaces used in the calculation. Methods for "relaxing" the nodes have been developed (Ceperley and Alder (1984)) and essentially exact solutions which are antisymmetric with respect to particle inerchange may be obtained.
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Quantum Monte Carlo: Faster, More Reliable, and More Accurate

Quantum Monte Carlo: Faster, More Reliable, and More Accurate

1. Pick an SCF wavefunction. This step will depend on your intuition and the process you want to model, but we discuss our experiences in this regard in Chapter 4. As we discuss there, we have found that extended MCSCF or CASSCF calculations do not necessarily work better due to the uncertainties in optimization. On the other hand, GVB wavefunctions are not sufficient for all problems, with the atomization of CN or NO as examples. If you are using a GVB wavefunction, then we would recommend using Jaguar [5] to make the wavefunction, since it does a good job at making initial guesses. In Appendix C we discuss and provide a script to convert a Jaguar wavefunction into a GAMESS wavefunction. We have found that GAMESS [6] is the most useful program available for producing wavefunctions because it is free, readily downloadable, under active development, and very flexible. One note is that we do not allow users to use an MCSCF calculation directly. Instead, following the recommendation from GAMESS, we require the user to run a CI calculation on the MCSCF natural orbitals to get the best CI coefficients possible. Be sure that you set the print cutoff low enough that GAMESS prints out enough determinants.
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Analyze Monte Carlo Simulation Applications for Project Management

Analyze Monte Carlo Simulation Applications for Project Management

Abstract: Risks have an important impact on construction comes in terms of its primary objectives. Construction comes that are tortuous in nature, uncertainty and risks within the same will develop from completely different sources. The record of the development trade isn't acceptable in terms of header up with risks incomes. Risk management is a process which consists of identification of risks, assessment with qualitatively and quantitatively, response with a suitable method for handling risks, and then control the risks by monitoring. This study proposes to use the risk management technique which has well - documented procedures for the one stop resolution all kinds of hazards possibly to occur throughout any construction project.
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Inhomogeneous backflow transformations in quantum Monte Carlo calculations

Inhomogeneous backflow transformations in quantum Monte Carlo calculations

HF nodes have been compared with either exact or very accurate nodes in a number of studies 关39,45,50–52兴. It has been found that the HF wave function often has too many nodal pockets for the ground states of atoms with four or more electrons. It is conceivable that coordinate transforma- tions could modify the number of nodal pockets of a wave function. However, we believe this to be unlikely for the backflow transformation presented in this paper, because this would require the backflow displacement field to be discon- tinuous at very specific configurations or exhibit other un- usual features. The development of a general backflow trans- formation with the appropriate discontinuities to correct HF nodes, which we have not attempted, seems likely to be a tremendously difficult task.
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Development of Monte Carlo Methods in Hypersonic Aerodynamics

Development of Monte Carlo Methods in Hypersonic Aerodynamics

A huge number of studies are devoted to the methods of the traditional use of statistical simulation. For this reason, we mainly consider aerodynamics problems in this paper. It has already been mentioned that the statistical methods are more efficient in practical problems of rarefied gas dynamics than the regular and semiregular methods. For the flow problems, which are the most important problems in aerodynamics, the statistical methods were successfully used for the calculation of aerodynamic characteristics of various (including very complex) bodies in free molecular and almost free molecular flows. The procedure, which was developed more than twenty years ago, is now implemented in standard computer programs and is widely used in many organizations. Applications in the case of smaller Knudsen ( )
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Quantum Monte Carlo simulations of solids

Quantum Monte Carlo simulations of solids

One way of reducing the computational cost is to in- clude only the most important determinants. This may be done by considering low-energy excitations from a reference determinant, which is normally the HF ground state. For example, if single and double excitations are included we have the configuration-interaction singles and doubles method, for which the computational cost scales as N 6 . Unfortunately, such truncated configuration-interaction methods are not size consis- tent; that is, the energy does not scale linearly with the number of electrons. For example, in a configuration- interaction singles and doubles calculation for N widely separated hydrogen molecules, the correlation energy increases only as 冑 N. Such a method is clearly unsuit- able for applications to solids. The size consistency prob- lem can be overcome via coupled-cluster expansions (C ˇ ı´zˇek, 1969). These implicitly include all excitations from the reference determinant, although the coeffi- cients in the expansion are approximated and the method is nonvariational. Coupled-cluster methods are capable of yielding highly accurate results and are direct competitors of QMC methods for molecular calcula- tions, but they are very expensive in large systems. For example, the computational cost of a CCSD calculation (coupled cluster with single and double excitations) scales as N 6 . Hartree-Fock and post-Hartree-Fock methods are described in a straightforward manner in the book by Szabo and Ostlund (1989).
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Quantum Monte Carlo Studies of Ultra-cold Molecules and Rashba Interactions.

Quantum Monte Carlo Studies of Ultra-cold Molecules and Rashba Interactions.

Quantum mechanics have been successful in interpreting the properties of condensed mat- ter systems at microscale. It describes the nature of fundamental particles in our daily world: atoms, molecules, electrons, etc. The Schr¨ odinger equation, proposed in 1925, pro- vide us an exact mathematical formulation of non-relativistic quantum physics . With the solution to the Schr¨ odinger equation, we are able to know and predict many properties of quantum systems. However, while physicists are often proud of being able to precisely describe how a system behaves quantitatively, solving the equations underlying the the- ory is a great challenge, especially when the size of the system becomes large. Thanks to the tremendous development in computer technology, many calculations which used to take immense time to perform, can be quickly done on modern computers. But there still exist many unsolved problems because they suffer difficulties beyond the computational efficiency. Theoretical physicists aim to exactly solve problems relying on the equations from first principles and many people have been devoting their lives to developing various kinds of methods. Quantum Monte Carlo, presented in this thesis, is one of the meth- ods to simulate quantum systems. Generally, the term “Monte Carlo” refers to a class of computer algorithms to compute the results based on random sampling. The word “quantum” indicates we are using the Monte Carlo technique to solve problems of quan- tum systems based on the knowledge of quantum mechanics. In this thesis, we will focus on electronic structure problems, a particular area of the quantum system problems.
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Ultra-fast Carrier Transport Simulation on the Grid. Quasi-Random Approach

Ultra-fast Carrier Transport Simulation on the Grid. Quasi-Random Approach

There are two main reasons for running this package on the computational Grid: (i) quantum problems are very computationally intensive; (ii) the inherently parallel nature of Monte Carlo applications makes efficient use of Grid resources. Grid (distributed) Monte Carlo applications require that the underlying random number streams in each subtask are independent in a statistical sense. The efficient application of quasi-Monte Carlo algorithms entails additional difficulties due to the possibility of job failures and the inhomogeneous nature of the Grid resource. In this paper we study the quasi-random approach in SALUTE and the performance of the corresponding algorithms on the grid, using the scrambled Halton, Sobol and Niederreiter sequences. A large number of tests have been performed on the EGEE and SEEGRID grid infrastructures using specially developed grid implementation scheme. Novel results for energy and density distribution, obtained in the inhomogeneous case with applied electric field are presented.
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Quasi Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems

Quasi Monte Carlo and multilevel Monte Carlo methods for computing posterior expectations in elliptic inverse problems

Typically, the posterior distribution is intractable, in the sense that direct sampling is unavail- able. One way to circumvent this problem is to use a Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution [40, 18, 11, 32, 9]. However, for large-scale applications where the number of input parameters is typically large and the solution of the forward model expensive, MCMC methods require careful tuning and may become infeasible in practice.

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A Quantum Monte Carlo approach to dark matter-nuclei interaction

A Quantum Monte Carlo approach to dark matter-nuclei interaction

In this chapter, we focus on the class of quantum Monte Carlo (QMC) methods, that in recent years have proven to be extremely successful in de- scribing properties of nuclei [44–46], from light to medium-heavy. In par- ticular, we will present the variational Monte Carlo (VMC) method, the phenomenological interaction we adopt for two- and three-nucleon, and the accurate nuclear wave functions used to calculate the differential cross sec- tion. We also give the calculated ground state energies E and proton point radii r p for the nuclei of interest, from d to 6 Li.
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Level Spectroscopy in Quantum Antiferromagnets Using Monte Carlo Simulations

Level Spectroscopy in Quantum Antiferromagnets Using Monte Carlo Simulations

temperature, a dimerized state that breaks no symmetries of the microscopic interactions can be stabilized for a lattice with two (or, a larger even number of) S = 1/2 spins per unit cell by introducing a pattern of different Heisenberg couplings in such a manner that favours singlet formation on dimers (or, larger units of an even number of spins). Such dimerized states preserves all the symmetries of the interactions and are dubbed quantum-disordered states to distinguish them from VBC states (that spontaneously break lattice symmetries) or spin liquid states (that have fractional excitations and topological order). Both the Shastry Sutherland model [11] and the bilayer Heisenberg model [33] (the latter will be discussed in more detail later) are Hamiltonians where such quantum-disordered ground states are realized. Quantum field theory techniques provide a very general framework for addressing low-energy properties of such interacting spin systems [13]. However, in many cases, while such an approach is really useful for the classification of possible phases on general grounds, several other details at the lattice level are important in deciding which phase is ultimately realized by a microscopic lattice Hamiltonian. Furthermore, any field theory has certain free parameters that have to be determined from a microscopic calculation to match its results (that apply at low-energy or equivalently at long distances) to an actual lattice model. Quantum Monte Carlo (QMC) methods provide an unbiased route for calculating properties of spin models that do not suffer from the notorious “sign problem” [14] for system sizes that typically far exceed other numerical methods like exact diagonalization. However, unlike exact diagonalization which gives access to all possible observables, QMC simulations typically allow for calculations of quantities like energy, uniform magnetic susceptibility, spin stiffness, and certain kinds of imaginary-time- dependent correlation functions. A general introduction to computational methods in quantum spin systems can be found in Ref. 15.
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Quantum Monte Carlo: Quest to Get Bigger, Faster, and Cheaper

Quantum Monte Carlo: Quest to Get Bigger, Faster, and Cheaper

det determinant of a is returned here ealeOK returns false if the calculation is singular and true otherwise Definition at line 161 of file LU.cpp.. Calculates the inverse of a matrix us[r]

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Energetics and dipole moment of transition metal monoxides by quantum Monte Carlo

Energetics and dipole moment of transition metal monoxides by quantum Monte Carlo

The transition metal 共 TM 兲 oxygen bond appears very prominently throughout chemistry and solid-state physics. Many materials, from biomolecules to ferroelectrics to the components of supernova remnants, contain this bond in some form. Many of these materials’ properties depend strongly on fine details of the TM–O bond, which makes accurate calculations of their properties very challenging. Here the authors report on highly accurate first principles calculations of the properties of TM monoxide molecules within fixed-node diffusion Monte Carlo and reptation Monte Carlo. © 2007 American Institute of Physics. 关DOI: 10.1063/1.2428294兴
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