SOURCE-LINKED INTELLIGENCE

Accelerated quantification of photolytic hydrogen using multi-fidelity Bayesian optimization and automation

CORDIS · observation · Publication date unknown

esting, especially as the search space expands exponentially. Self-driving laboratories (SDLs) are poised to revolutionize this field by leveraging advancements in robotics, computational power, and artificial intelligence (AI). SDLs can achieve scientific objectives hundreds of times faster than traditional automation, integrating hardware for experiment execution and software for data analysis and subsequent experiment design. Despite these advancements, the time-intensive steps of photolysis and gas analysis remain bottlenecks. This proposal addresses the challenge of accelerating the photolysis process beyond current SDL capabilities. By employing a multi-fidelity Bayesian optimization algorithm, I aim to reduce the frequency of crucial yet time-intensive steps in photocatalysis. This novel approach, untested in real photolysis experiments, has the potential to extend broadly to other areas of electrochemistry, including CO2/N2 electrolysis. Self-driving laboratories, Automation, clean energy, multi-fidelity algorithm

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR