Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method to precisely control the molecular weight distribution in polymer synthesis via ATRP, enabling the creation of diverse and targeted polymer shapes.

Contribution

It presents a novel simulation-based RL approach for controlling ATRP reactions to achieve specific MWD shapes, including Gaussian and bimodal distributions.

Findings

RL controllers successfully optimize MWD shapes

Control policies are robust to reaction variations

Method enables targeted polymer design

Abstract

We devise a novel technique to control the shape of polymer molecular weight distributions (MWDs) in atom transfer radical polymerization (ATRP). This technique makes use of recent advances in both simulation-based, model-free reinforcement learning (RL) and the numerical simulation of ATRP. A simulation of ATRP is built that allows an RL controller to add chemical reagents throughout the course of the reaction. The RL controller incorporates fully-connected and convolutional neural network architectures and bases its decision upon the current status of the ATRP reaction. The initial, untrained, controller leads to ending MWDs with large variability, allowing the RL algorithm to explore a large search space. When trained using an actor-critic algorithm, the RL controller is able to discover and optimize control policies that lead to a variety of target MWDs. The target MWDs include Gaussians of various width, and more diverse shapes such as bimodal distributions. The learned control policies are robust and transfer to similar but not identical ATRP reaction settings, even under the presence of simulated noise. We believe this work is a proof-of-concept for employing modern artificial intelligence techniques in the synthesis of new functional polymer materials.