Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these technologies were employed within a computationally controlled flow reactor which enabled self-optimisation of a range of RAFT polymerisation formulations. This allowed for autonomous identification of optimum reaction conditions to afford targeted polymer properties - the first demonstration of closed loop ( i.e. user-free) optimisation for multiple objectives in polymer synthesis. The synthesis platform comprised a computer-controlled flow reactor, online benchtop NMR and inline gel permeation chromatography (GPC). The RAFT polymerisation of tert -butyl acrylamide ( t BuAm), n -butyl acrylate (BuA) and methyl methacrylate (MMA) were optimised using the Thompson sampling efficient multi-objective optimisation (TSEMO) algorithm which explored the trade-off between molar mass dispersity ( ) and monomer conversion without user interaction. The pressurised computer-controlled flow reactor allowed for polymerisation in normally "forbidden" conditions - without degassing and at temperatures higher than the normal boiling point of the solvent. Autonomous experimentation included comparison of five different RAFT agents for the polymerisation of t BuAm, an investigation into the effects of polymerisation inhibition using BuA and intensification of the otherwise slow MMA polymerisation. An artificially intelligent flow-reactor platform equipped with online NMR and GPC enables autonomous polymerisation optimisation utilising a machine learning algorithm to map the trade-off between monomer conversion and dispersity.