Monte Carlo molecular modeling

About: Monte Carlo molecular modeling is a research topic. Over the lifetime, 11307 publications have been published within this topic receiving 409122 citations.

Study of the structure of molecular complexes. VII. Effect of correlation energy corrections to the Hartree‐Fock water‐water potential on Monte Carlo simulations of liquid water

Abstract: The two‐body Hartree‐Fock potential for water‐water interactions previously used to describe small water clusters and used in Monte Carlo studies of liquid water has been used to assess the effect of neglecting the correlation energy correction. A number of semiempirical corrections based on the London, Kirkwood, and Wigner formulas were used. They have an energy spread such that they encompass the expected magnitude of the exact correlation energy correction. Short range correlation interactions were included using a modification of the functional of density proposed by Wigner and the corresponding long range interactions were given by a term proportional to r−6 with coefficients due to both London and Kirkwood. After adding the corrections to the Hartree‐Fock potential the various potentials were compared by generating a number of potential energy surfaces. Following this the water‐water potentials were used in a Monte Carlo simulation of liquid water to calculate thermodynamic and structural data. Afte...

Introduction: Theory and “Technical” Aspects of Monte Carlo Simulations

Abstract: An outline is given of the physical problems which can be treated by Monte Carlo sampling and which are described in the later chapters of this book. Then the theoretical background is described for the application of this technique to calculate statistical ensemble averages of classical interacting many-body systems. The practical realization of the method is discussed, as well as its limitations due to finite time averaging, finite size and boundary effects, etc. It is shown how to extract meaningful information from the “raw data” of such a “computer experiment”. The stochastic simulation of kinetic processes is also treated, with particular emphasis on the interpretation of the results near phase transitions in the system. Finally some approximative variants of the technique are discussed which might become useful to simulate critical phenomena.

Algorithmic aspects of multicanonical simulations

Abstract: Monte Carlo (MC) simulations of many systems, can be considerably speeded up by using multicanonical or related methods. I shall focus on two aspects: (i) Opinions about the optimal choice of weights. (ii) Recursive weight factor estimates.

An Unfolding Method for High Energy Physics Experiments

Abstract: Finite detector resolution and limited acceptance require one to apply unfolding methods in high energy physics experiments. Information on the detector resolution is usually given by a set of Monte Carlo events. Based on the experience with a widely used unfolding program (RUN) a modified method has been developed. The first step of the method is a maximum likelihood fit of the Monte Carlo distributions to the measured distribution in one, two or three dimensions; the finite statistics of the Monte Carlo events is taken into account by the use of Barlow’s method with a new method of solution. A clustering method is used before combining bins in sparsely populated areas. In the second step a regularization is applied to the solution, which introduces only a small bias. The regularization parameter is determined from the data after a diagonalization and rotation procedure. 1 THE UNFOLDING PROBLEM A standard task in high energy physics experiments is the measurement of a distribution of some kinematical quantity . With an ideal detector one could measure the quantity in every event and could obtain by a simple histogram of the quantity . With real detectors the determination of is complicated by three effects: Limited acceptance: The probability to observe a given event, the detector acceptance, is less than 1. The acceptance depends on the kinematical variable . Transformation: Instead of the quantity a different, but related quantity is measured. The transformation from to can be caused by the non-linear response of a detector component. Finite resolution: The measured quantity is smeared out due to the finite resolution (or limited measurement accuracy) of the detector. Thus there is only a statistical relation between the true kinematical variable and the measured quantity . The really difficult effect in the data correction for experimental effects, or data transformation from to is the finite resolution, causing a smearing of the measured quantities. Mathematically the relation between the distribution of the true variable , to be determined in an experiment, and the measured distribution of the measured quantity is given by the integral equation, d (1) called a Fredholm integral equation of the first kind. In practice often a known (measured or simulated) background contribution has to be added to the right-hand side of equation (1); this contribution is ignored in this paper. The resolution function represents the effect of the detector. For a given value the function describes the response of the detector in the variable for that fixed value . The problem in determining the distribution from measured distributions is called unfolding; it is called an inverse problem. Unfolding of course requires the knowledge of the resolution function , i.e. all the effects of limited acceptance, transformation and finite resolution.