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Autonomous optimisation of general conditions for novel reaction

CORDIS · observation · Publication date unknown

AOGCNR, proposes to develop a fully autonomous, data-driven methodology for optimising general conditions for a novel organic catalysis reaction. It integrates a self-driving laboratory (SDL) with a Large Language Model (LLM)-enhanced Bayesian Optimisation framework, combining cutting-edge robotics, machine learning and expert chemical reasoning. A two-phase approach is proposed. The first phase uses a statistical pipeline to screen a vast chemical space of over 3 million combinations, reducing it by over 97% to a manageable subspace. The second phase deploys the LLM-enhanced Bayesian Optimisation algorithm within this refined space to autonomously identify general reaction conditions that perform well across multiple substrates. This approach represents a significant advancement over current methods, which typically focus on single-substrate optimisation. The methodology will be validated on both a known reaction and a novel cyclisation discovered by researchers at the University of Liverpool, demonstrating its ability to accelerate the discovery of novel catalytic reactions. This

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR