The interacting growth model (IGW) belongs to a class of growth models used to simulate polymerization processes. In this work it was investigated by means of Monte Carlo simulations and a statistical analysis of the properties of the self avoiding configurations was made. We evaluated the distributions of growths frustrated by self trapping for growths carried out under different temperatures. The IGW model generates long chains at low temperatures and its efficiency is improved when the temperature is lowered. This fact contrasts with the usual methods of generating self avoiding chain based on models of self avoinding walks (SAW). Using the distributions of the configurations generated and the fractions and successful growth we evaluated the mean lengths of growths and the distribution of contacts as a function of the temperature. The results confirm the findings of Narasimhan et al. that there is a transition Θ equivalent to that observed in SAW models and complements the analysis of the model defined on square networks.
growth models; theta-transition; self-avoiding walks
