Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with...

The application of artificial intelligence and machine learning is revolutionising the chemical industry, with the ability to automate and self-optimise reactions facilitating a step change in capability. Unlike small-molecules, polymer nanoparticles require navigation of a more complex parameter space to access the desired performance. In addition to the chemical reaction, it is desirable to optimise the polymer molecular weight distribution, particle size and polydispersity index. To solve this many-objective optimisation problem, a self-driving laboratory is constructed which synthesises and characterises polymer nanoparticles (incorporating NMR spectroscopy, gel permeation chromatography and dynamic light scattering). This facilitates the autonomous exploration of parameter space with programmable screens or AI driven optimisation campaigns via a cloud-based framework. The RAFT polymerisation of diacetone acrylamide mediated by a poly(dimethylacrylamide) macro-CTA was optimised to maximise monomer conversion, minimise molar mass dispersity, and target 80 nm particles with minimised polydispersity index. A full-factorial screen between 6- and 30-minutes residence time, between 68 and 80 °C and between 100 and 600 for the [monomer] : [CTA] ratio enabled mapping of the reaction space. This facilitated in-silico simulations using a range of algorithms - Thompson sampling efficient multi-objective optimisation (TSEMO), radial basis function neural network/reference vector evolutionary algorithm (RBFNN/RVEA) and multi objective particle swarm optimisation, hybridised with an evolutionary algorithm (EA-MOPSO), which were then applied to in-lab optimisations. This approach accounts for an unprecedented number of objectives for closed-loop optimisation of a synthetic polymerisation; and enabled the use of algorithms operated from different geographical locations to the reactor platform.