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.

该研究提出了一种创新的人工智能工作流，结合了大型语言模型、贝叶斯优化和主动学习循环，以加快和增强催化剂优化。该方法将先进语言理解与强大的优化策略相结合，将从各种文献中提取的知识有效地转化为可行的实验和优化参数。在本文中，我们展示了该 AI 工作流在氨合成催化剂优化中的应用，结果突显了该工作流在简化催化剂开发过程方面的能力，提供了一种快速、资源高效且高精度的替代传统方法的选择。