Computational Generation of Polymer Chains For the development of polymeric amorphous materials with desired properties new algorithms are needed to help in the process of rationalizing the relationship between the atomic structure and the macroscopic observables. Even though numerical simulations, such as Molecular Dynamics, have become an efficient tool for giving a detailed description of the structural changes in atomic arrangements under different temperature and pressure, the computation time that can be simulated are below the time span of relevant physical and chemical phenomena. In this work an algorithm to replicate the equilibrium structure in linear homopolymers is developed. The algorithm focuses on performing local movements of dihedral angles to minimize the total potential energy. The procedure generates chains of arbitrary length in a simulation volume with the constrain of a fixed endtoend radius. Preliminary results show that the algorithm succeed in decreasing the potential energy substantially while approximating the target averaged endtoend radius asymptotically.

Predictive Modeling for Geometric RuleBased Methods Previously, we proposed a method for incorporating molecular geometry in a biological rulebased model by encoding molecular curvature into the rules and associated binding rate constants. We combined this method with a 3D rigidbody Monte Carlo simulation to model antigenantibody aggregation. In this work, we use our geometric rulebased method to develop a model for predicting the output of the fullresolution Monte Carlo simulation given the output of lower resolution simulations. The purpose of this predictive model is to reduce the computational cost of the Monte Carlo simulation. We develop this model by first choosing a rule set for each molecular geometry and varying only the binding rate constant for each Monte Carlo resolution, and then fitting the resulting data to a function. We examine the calculation time needed for each predictive model to demonstrate how this model is more efficient than running a fullresolution simulation. We find that this method can reduce the computational time of the Monte Carlo simulation by up to 20%.