Integrating mechanistic modelling with Bayesian optimisation: accelerated self-driving laboratories for RAFT polymerisation

Abstract

Discovery of sustainable, high-performing materials on timescales to meet societal needs is only going to be achieved with the assistance of artificial intelligence and machine learning. Herein, a Bayesian optimisation algorithm is trained using in silico reactions facilitated by a new mechanistic model for reversible addition fragmentation chain transfer polymerisation (RAFT). This subsequently informs experimental multi-objective self-optimisation of RAFT polymerisation using an automated polymerisation platform capable of measuring the critical algorithm objectives (monomer conversion and molecular weight distribution) online. The platform autonomously identifies the Pareto-front representing the trade-off between monomer conversion and molar mass dispersity with a reduced number of reactions compared to the equivalent fully experimental optimisation process. This model-informed AI approach provides opportunities for much more sustainable and efficient discovery of polymeric materials and provides a benchmark for other complex chemical systems.