Issue 3, 2018

Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

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.

Graphical abstract: Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

Article information

Article type
Paper
Submitted
10 दिसम्बर 2017
Accepted
01 मार्च 2018
First published
01 मार्च 2018

Mol. Syst. Des. Eng., 2018,3, 496-508

Author version available

Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

H. Li, C. R. Collins, T. G. Ribelli, K. Matyjaszewski, G. J. Gordon, T. Kowalewski and D. J. Yaron, Mol. Syst. Des. Eng., 2018, 3, 496 DOI: 10.1039/C7ME00131B

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements