Search Strategy for Rare Microstructure to Optimize Material Properties of Filled Rubber

A shortcut to understand the microstructure-property relationship is sampling and analysis of microstructures that induce the desired material property. In the case of filled rubber, the simulation of complex filler morphology involves hundreds of filler particles. This requires a large amount of iterative sampling, because the number of parameters is when using coordinates of the n particles as the search objective. Furthermore, the morphology that induces the desired property, e.g. extremely high modulus, only occurs rarely. In this paper, we propose an effective three-step search method for the filler morphology. In the first step, the replica exchange Markov chain Monte Carlo (MCMC) was employed to discretely search among a wide range of morphologies. In this step, we reduced the filler morphology space in sampling by introducing distributed filler candidate points and spin function. In the second step, the gradient descent method was applied to search for the desired morphology locally in the high-dimensional space , starting from the morphologies obtained by the replica exchange MCMC. Lastly, the coarse-grained molecular dynamics (CGMD) simulations were performed to validate the morphologies actually show the desired properties, because the surrogate model of CGMD was employed in the first 2 steps for the efficient search. Using the proposed method, we demonstrate the search for morphologies that induce high elastic modulus.