Self-driving lab for the data-driven design of single-chain polymer nanoparticles

Rationally designed single-chain polymer nanoparticles (SCNPs) with self-assembling characteristics provide the necessary structural complexity for use as protein mimics while granting access to a broad chemical design space, straightforward preparation, and tunable properties. Here, we describe a high-throughput, autonomous workflow for active learning to discover structure-property relationships and iteratively predict and synthesize SCNPs. We developed a control system comprising a liquid-handling robot, a custom-built lightbox to catalyze photoinduced electron/energy transfer reverse addition-fragmentation chain transfer (PET-RAFT) polymerization, a dynamic light scattering (DLS) plate reader, and a robotic arm. We developed rationally designed, randomly sampled seed libraries as training sets for Gaussian process regressor (GPR) models. Over multiple generations of Bayesian optimization (BO), additional generations of polymer synthesis were found to improve model performance and represent the impact of specific monomer content on Rh. This automated polymer discovery platform serves as a useful prototype for designing SCNPs with structures tailored for biomedical applications.