Molecular modeling Monte Carlo sampling

The hst which follows gives an outline of the properties of a Monte Carlo simulation used in the context of molecular modeling studies for sampling either multiple conformations of smaller, flexible stmctures or multiple local minima of larger macromolecules or polymers ... 

New model polymers were randomly generated and reacted until the predetermined Monte Carlo sample size was reached. The quantities of all products at each time were added to the sum recorded from all previous polymers. Afterward, the weight yield in g/giignin of each product of interest was calculated at each time step by multiplying its cumulative product sum by the ratio of its molecular weight and the cumulative molecular weight of all the model polymers reacted. The results were organized as product yield versus isothermal batch reaction time. 

Monte Carlo (MC) techniques for molecular simulations have a long and rich history, and have been used to a great extent in studying the chemical physics of polymers. The majority of molecular modeling studies today do not involve the use of MC methods however, the sampling capabiUty provided by MC methods has gained some popularity among computational chemists as a result of various studies (95—97). Relevant concepts of MC are summarized herein. 

The calculation of the potential of mean force, AF(z), along the reaction coordinate z, requires statistical sampling by Monte Carlo or molecular dynamics simulations that incorporate nuclear quantum effects employing an adequate potential energy function. In our approach, we use combined QM/MM methods to describe the potential energy function and Feynman path integral approaches to model nuclear quantum effects. 

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