High-Throughput Automated Workflow Development for AI Approaches in Chemistry

This project will focus on the development of a generalisable automated workflow for organic synthesis, and on exploiting automated high-throughput screening and characterisation to curate and provide the crucial experimental data required for chemical discovery through generative AI to identify novel molecule and materials classes, and for the development of new multi-fidelity machine learning approaches for active learning, Bayesian optimisation, and design of experiments to uncover reaction mechanisms.