Work

Harnessing data and control with AI/ML-driven polymerization and copolymerization

Year: 2025

Type: article

Abstract: 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.

Source: Faraday Discussions

DOI: 10.1039/d5fd00066a