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Issue 3, 2018
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Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

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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

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Publication details

The article was received on 10 Dec 2017, accepted on 01 Mar 2018 and first published on 01 Mar 2018


Article type: Paper
DOI: 10.1039/C7ME00131B
Citation: Mol. Syst. Des. Eng., 2018,3, 496-508
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    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

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