Accelerating Langevin Field-Theoretic Simulation with Semantic Segmentation Model

Abstract

Langevin field-theoretic simulation (L-FTS) can account for the fluctuation effect in a polymer system which is ignored in the self-consistent mean-field theory. Even though L-FTS is computationally efficient compared to traditional particle-based polymer simulations, it requires large computational demand. In order to accelerate the L-FTS, we introduce deep learning (DL). In L-FTS, the functional integral over the pressure field is evaluated using saddle-point approximation whereas the exchange field fluctuates according to the Langevin equation. Using convolutional neural networks for the semantic segmentation task in the computer vision, we directly generate saddle point pressure field for given exchange field. By combining DL and Anderson mixing method, we successfully reduce the number of iterations for finding saddle points, and achieve speedup of 2 ~3 compared to the Anderson-mixing-only method without sacrifice of accuracy. Our approach is very versatile and efficient enough to be applied to a variety of systems without prior data collection and pre-trained neural network.