催化剂设计与优化的人工智能（AI）工作流
An Artificial Intelligence (AI) workflow for catalyst design and optimization
AI总结：
提出集成大语言模型、贝叶斯优化与主动学习的AI工作流，用于催化剂设计优化，并在氨合成中验证其高效高精度。
摘要：
在寻求新型催化剂开发以应对紧迫的环境问题和能源需求的过程中，传统设计与优化方法往往因催化剂参数空间的复杂性和广阔性而力不从心。机器学习（ML）的出现为催化剂优化领域开启了新时代，为传统技术的缺陷提供了潜在解决方案。然而，现有方法未能有效利用日益增长的催化剂合成科学文献中所蕴含的丰富信息。为弥补这一不足，本研究提出了一种创新的人工智能（AI）工作流，该工作流集成了大型语言模型（LLMs）、贝叶斯优化和主动学习循环，以加速并增强催化剂优化。我们的方法将先进的语言理解与稳健的优化策略相结合，有效将来自不同文献的知识转化为可操作的参数，用于实际实验和优化。在本文中，我们展示了该AI工作流在氨生产催化剂合成优化中的应用。结果凸显了该工作流简化催化剂开发过程的能力，为传统方法提供了一种快速、资源高效且高精度的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
