AI-Empowered Catalyst Discovery: A Survey from Classical Machine Learning Approaches to Large Language Models

作者：Yuanyuan Xu, Hanchen Wang, Wenjie Zhang, Lexing Xie, Yin Chen, Flora D. Salim, Ying Zhang, J. Justin Gooding, Toby Walsh · 发表于：arXiv (Cornell University) · 年份：2025 · DOI：10.48550/arxiv.2502.13626 · 被引用次数：3 · 研究领域：Machine Learning in Materials Science、Topic Modeling

Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in computational chemistry and has garnered significant attention due to the promising performance of advanced Artificial Intelligence (AI) techniques. The development of Large Language Models (LLMs) notably accelerates progress in the discovery of both homogeneous and heterogeneous catalysts, where their chemical reactions differ significantly in material phases, temperature, dynamics, etc. However, there is currently no comprehensive survey that discusses the progress and latest developments in both areas, particularly with the application of LLM techniques. To address this gap, this paper presents a thorough and systematic survey of AI-empowered catalyst discovery, employing a unified and general categorization for homogeneous and heterogeneous catalysts. We examine the progress of AI-empowered catalyst discovery, highlighting their individual advantages and disadvantages, and discuss the challenges faced in this field. Furthermore, we suggest potential directions for future research from the perspective of computer science. Our goal is to assist researchers in computational chemistry, computer science, and related fields in easily tracking the latest advancements, providing a clear overview and roadmap of this area. We also organize and make ac...