Harnessing Data and Control with AI/ML-driven Polymerization and Copolymerization

Abstract

Creating and curating new data to augment heuristics is a forthcoming approach to materials science in the future. Highly improved properties are advantageous even with “commodity polymers” that do not need to undergo new synthesis, high-temperature processes, or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing synthesis and manufacturing methods will enable higher throughput and innovative directed experiments. Simulation and modeling to create digital twins with statistical and logic-derived design, such as the design of experiments (DOE), will be superior to trial-and-error approaches when working with polymer materials. This paper describes and demonstrates protocols for understanding hierarchical approaches in optimizing the polymerization and copolymerization process via AI/ML to target specific properties. The key is self-driving continuous flow chemistry reactors with sensors (instruments) and real-time ML with an online monitoring set-up that allows a feedback loop mechanism. We provide initial results using ML refinement of the classical Mayo-Lewis equation (MLE), time-series data, and an autonomous flow reactor system build-up as a future data-generating station. More importantly, it lays the ground for precision control of the copolymerization process. In the future, it should be possible to undertake collaborative human-AI-guided protocols for the autonomous fabrication of new polymers guided by literature and available data sources targeting new properties.

Article information

Article type
Paper
Submitted
03 May 2025
Accepted
11 Aug 2025
First published
12 Aug 2025
This article is Open Access
Creative Commons BY license

Faraday Discuss., 2025, Accepted Manuscript

Harnessing Data and Control with AI/ML-driven Polymerization and Copolymerization

R. Advincula, I. N. Ivanov, R. K. Vasudevan, R. Kumar, P. Christakopoulos, M. Tsakinika, J. Chen, J. M. Y. Carrillo, Q. Zhu and B. G. Sumpter, Faraday Discuss., 2025, Accepted Manuscript , DOI: 10.1039/D5FD00066A

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