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Molecular Modelling—an Enabling Technology for Chemical Engineers
Author(s) -
Choi Phillip
Publication year - 2006
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450840301
Subject(s) - polyolefin , statistical physics , miscibility , molecular dynamics , scale (ratio) , monte carlo method , set (abstract data type) , field (mathematics) , force field (fiction) , computer science , mixing (physics) , nanotechnology , materials science , physics , mathematics , chemistry , computational chemistry , polymer , artificial intelligence , programming language , statistics , quantum mechanics , layer (electronics) , pure mathematics , composite material
This article briefly describes the basic concepts involved in the two most commonly used molecular modelling methods—molecular dynamics (MD) and Monte Carlo (MC). The methods are particularly useful for studying structures at the length scale of nanometre. Two examples (both are on the study of the miscibility of polyolefin blends) are used to illustrate the techniques. It is demonstrated that it is the nano‐scaled structures formed by the segments of the constituent polyolefins that prevent them from mixing with each other. The examples also show that selection of specific method (MD or MC) depends on the nature of the problem in hand. In general, MC is more efficient than MD in terms of generating equilibrated structure while MD can provide information about the dynamics of a system. This is simply because MD requires the solution of equations of motion (a set of second order differential equations) while MC does not. Nonetheless, both methods need a reasonably accurate force field.