  In the pursuit of novel catalyst development to address pressing
environmental concerns and energy demand, conventional design and optimization
methods often fall short due to the complexity and vastness of the catalyst
parameter space. The advent of Machine Learning (ML) has ushered in a new era
in the field of catalyst optimization, offering potential solutions to the
shortcomings of traditional techniques. However, existing methods fail to
effectively harness the wealth of information contained within the burgeoning
body of scientific literature on catalyst synthesis. To address this gap, this
study proposes an innovative Artificial Intelligence (AI) workflow that
integrates Large Language Models (LLMs), Bayesian optimization, and an active
learning loop to expedite and enhance catalyst optimization. Our methodology
combines advanced language understanding with robust optimization strategies,
effectively translating knowledge extracted from diverse literature into
actionable parameters for practical experimentation and optimization. In this
article, we demonstrate the application of this AI workflow in the optimization
of catalyst synthesis for ammonia production. The results underscore the
workflow's ability to streamline the catalyst development process, offering a
swift, resource-efficient, and high-precision alternative to conventional
methods.
