Monte Carlo Method is the common denomination of a class of computational methods with which one can calculate macroscopic configurational properties of pure and mixtures from given force field model. Such methods are helpful in understanding observed phenomena and can possibly predict/extrapolate observations that would be made in conditions of difficult experimental access. The increasing need of understanding mutual effects of phenomena occuring in different scales has been making Molecular Simulation a tool of great value in Chemical Engineering research. Possible applications are the calculation of fluid properties, the study of phase equilibrium, the study of interactions between biomolecules and pharmaceutical compounds, among many others.

The course Statistical Thermodynamics: Monte Carlo is the second course in the launched series in School of Advanced Studies in Applied Thermodynamics organized by the Chemical Engineering Program of the Alberto Luiz Coimbra Institute (COPPE/UFRJ/Brazil). The course aims at complementing the academic formation of Chemical Engineering professors and researchers by offering a practical perspective on Molecular Simulation. This is intended to incite the use of Molecular Simulation as an auxiliary tool in the ongoing research activity of each participant.

The course is primarily devoted to professors of undergratuate and graduate programs of Chemical Engineering and related areas. Research associates, post-doctoral fellows and graduate students of such programs are also welcome.

D. N. Theodorou, Progress and Outlook in Monte Carlo Simulations, Ind. Eng. Chem. Res., 49 (7), 3047-3058, (2010).

K. E. Gubbins and J. D. Moore, Molecular Modeling of Matter: Impact and Prospects in Engineering, Ind. Eng. Chem. Res. 49 (7), pp 3026-3046 (2010).

E. J. Maginn and J. R. Elliott, Historical Perspective and Current Outlook for Molecular Dynamics as a Chemical Engineering Tool, Ind. Eng. Chem. Res. 49 (7), pp 3059-3078 (2010).

D. Frenkel and B. Smit, "Understanding molecular simulation from algorithms to applications". 2nd Edition, Academic Press, 2002.