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, using model monomers such as styrene and acrylate. 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.
| Original language | English |
|---|---|
| Pages (from-to) | 478-499 |
| Number of pages | 22 |
| Journal | Faraday Discussions |
| Volume | 262 |
| DOIs | |
| State | Published - Jan 1 2026 |
Funding
This work was performed at the Center for Nanophase Materials Sciences (CNMS), a US Department of Energy Office of Science User Facility at Oak Ridge National Laboratory (ORNL). The INTERSECT Initiative supported the development of the autonomous flow reactor system as part of the Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. We also acknowledge support from the Governor’s Chair Professor funds, University of Tennessee for R. C. Advincula. This manuscript has been co-authored by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/downloads/doe-public-access-plan ).