Fig. 5: Schematic representation of the framework is shown.
A hybrid optimization approach has been used to develop interaction parameters for coarse-graining polymer molecules using machine learning techniques. The framework includes the bottom-up optimization approach (detail given in MD simulation details section) for predicting bonded interaction parameters (\({k}_{l}^{{\rm {CG-PTMO}}},{l}_{0}^{{\rm {CG-PTMO}}},{k}_{\theta }^{{\rm {CG-PTMO}}},{\theta }_{0}^{{\rm {CG-PTMO}}}\)) by matching bond (\({P}_{l}^{{\rm {UA-PTMO}}}\)) and angle distributions (\({P}_{\theta }^{{\rm {UA-PTMO}}}\)) from a united-atom model of PTMO as ground truth. In the bonded optimization process, the DNN was trained using bond (\({P}_{l}^{{\rm {PCG}}}(l)\)) and angle (\({P}_{\theta }^{{\rm {PCG}}}(\theta )\)) distribution data from prototype-coarse-grained (PCG) simulations. The top-down approach (MD simulation details) was incorporated with a genetic algorithm and deep neural network for predicting the nonbonded interaction parameters (σCG−PTMO, ϵCG−PTMO) of the CG model. The DNN was trained using parameters (\({k}_{l}^{{\rm {PCG}}},{l}_{0}^{{\rm {PCG}}},{k}_{\theta }^{{\rm {PCG}}},{\theta }_{0}^{{\rm {PCG}}},{\epsilon }^{{\rm {PCG}}},{\sigma }^{{\rm {PCG}}}\), and T) acquired from PCG simulations and was integrated into GA along with parameters obtained in the bottom-up step to predict the density. Consecutively, nonbonded interaction parameters (ϵCG−PTMO, σCG−PTMO) were optimized by matching temperature-dependent experimental density. The dashed green arrows indicate the optimal parameters i.e., machine-learned CG parameters, obtained using the bottom-up and top-down portions of the strategy.
