Accelerating Langevin Field-Theoretic Simulation of Polymers with Deep Learning

Abstract

Langevin field-theoretic simulation (L-FTS) is a promising tool in polymer field theory that can account for the compositional fluctuation effect, which is neglected in the self-consistent field theory (SCFT). However, L-FTS is a computationally expensive tool, and it may take more than a week to accurately calculate ensemble averages of thermodynamic quantities. In our previous study, we introduced a deep neural network (DNN) that estimates the saddle point of the pressure field to reduce the subsequent Anderson mixing (AM) iterations. Herein, we propose a novel DNN that can be successively applied to determine the saddle point without using conventional field-update algorithms. Major deep learning (DL) models for semantic segmentation in computer vision are adopted to construct the optimal DNN architecture. Our model utilizing atrous convolutions in parallel is accurate and computationally efficient, and it is robust to the simulation parameter changes and can consequently be reused after single training. We demonstrate that our DNN can achieve speedup of factor 6 or more compared to the AM method without affecting accuracy. Open-source code for our deep Langevin FTS (DL-FTS) enables an easy and rapid Python scripting of SCFT and L-FTS incorporated with CPU or GPU parallelization and DL.