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DMC: A multifunctional hybrid dynamics/Monte Carlo simulation algorithm for the evaluation of conformational space
Author(s) -
Morley S.D.,
Jackson D.E.,
Saunders M.R.,
Vinter J.G.
Publication year - 1992
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540130604
Subject(s) - cyclooctane , simulated annealing , monte carlo method , molecular dynamics , statistical physics , cyclopentane , parallel tempering , monte carlo algorithm , sampling (signal processing) , dynamic monte carlo method , umbrella sampling , force field (fiction) , kinetic monte carlo , metropolis–hastings algorithm , hybrid monte carlo , algorithm , chemistry , computer science , markov chain monte carlo , physics , computational chemistry , cyclohexane , mathematics , statistics , organic chemistry , filter (signal processing) , artificial intelligence , computer vision
A hybrid conformational search algorithm (DMC) is described that combines a modified form of molecular dynamics with Metropolis Monte Carlo sampling, using the COSMIC(90) force field. Trial configurations are generated by short bursts of high‐temperature dynamics in which the initial kinetic energy is focused into single bond rotations or alternatively into “corner‐flapping” motions in ring systems. Constant temperature and simulated annealing search protocols have been applied to the conformational analysis of several model hydrocarbons (cyclopentane, cyclohexane, cycloheptane, cyclooctane, cycloheptadecane, decane, and tetradecane), and the performance compared with conventional molecular dynamics and Monte Carlo sampling methods. Optimum Metropolis sampling temperatures have been determined and range from 1000–2000 K for acyclic molecules to 3000 K for cyclic systems. Simulated annealing runs are most successful at locating the global minimum when cooling slowing from these optimum temperatures.