出版社：Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
摘要：The computational study of intermolecular relationships of a given material can be used as a route for predicting quantities impossible or difficult to be determined experimentally. Furthermore properties of new materials can also be predicted by techniques of this type, when they are still in the modeling phase. This technique reproduces the classical dynamic relationships between the constituent elements of the material, atoms or unicorpuscular approximations of molecules, from interaction potential models called force fields. This work aims to develop a tool that performs the composition of linear polymeric chain systems through a self-avoided walk. For this, the concept of self-experimentation of long walks (SAWLC) was used, together with the Python language to develop MpolSys Modeler. This tool is a non-overlapping polymer chain generator, which in turn generates outputs that can be used as input to Moltemplate. To validate the tool's results, experiments were carried out in which the numbers and polymerization chains of the simulated polymer were varied, observing the overlap or not of the molecules that make up the simulation. At the end of the simulations, there were positive results that indicate a promising usage of the tool for the creation of polymers with a high number of chains and degrees of polymerization.
