Monte Carlo histogram method

Figure 1.10. The figure demonstrates what is obtained when the Monte Carlo method (cf. Section 3.5.5) is used to simulate normally distributed values each histogram (cf. Section 1.8.1) summarizes 100 measurements obviously, many do not even come close to what one expects under the label bell curve. ...

Tsai, C.J. Jordan, K.D., Use of the histogram and jump-walking methods for overcoming slow barrier crossing behavior in Monte Carlo simulations applications to the phase transitions in the (Ar)i3 and (FbOjs clusters, J. Chem. Phys. 1993, 99, 6957... 

Among the methods discussed in this book, FEP is the most commonly used to carry out alchemical transformations described in Sect. 2.8 of Chap. 2. Probability distribution and TI methods, in conjunction with MD, are favored if there is an order parameter in the system, defined as a dynamical variable. Among these methods, ABF, derived in Chap. 4, appears to be nearly optimal. Its accuracy, however, has not been tested critically for systems that relax slowly along the degrees of freedom perpendicular to the order parameter. Adaptive histogram approaches, primarily used in Monte Carlo simulations - e.g., multicanonical, WL and, in particular, the transition matrix method - yield superior results in applications to phase transitions,... 

Bouzida D, Rejto PA, Verkhivker GM(1999) Monte Carlo simulations of ligand-protein binding energy landscapes with the weighted histogram analysis method, Int I Quantum Chem, 73 113—121... 

IV.3. Monte-Carlo data analysis with the weighted histogram method... 

The energy landscape approach can elucidate such general properties of molecular recognition as the nature of the thermodynamic phases and barriers on the ligand-protein association pathway [127,128]. This method evaluates equilibrium thermodynamic properties of the system from Monte Carlo simulations of the system at a broad temperature range with the aid of the optimized data analysis and the weighted histogram analysis technique [148-153],... 

We have studied the phase and micellization behavior of a series of model surfactant systems using Monte Carlo simulations on cubic lattices of coordination number z = 26. The phase behavior and thermodynamic properties were studied through the use of histogram reweighting methods, and the nanostructure formation was studied through examination ofthe behavior ofthe osmotic pressure as a function of composition and through analysis of configurations. 

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