AI-Empowered Catalyst Discovery: A Survey from Classical Machine Learning Approaches to Large Language Models
Yuanyuan Xu, Hanchen Wang, Wenjie Zhang, Lexing Xie, Yin Chen, Flora, Salim, Ying Zhang, Justin Gooding, Toby Walsh

TL;DR
This survey reviews recent advances in AI-driven catalyst discovery, focusing on classical machine learning and large language models, highlighting their advantages, challenges, and future research directions.
Contribution
It provides the first comprehensive overview of AI techniques, including LLMs, applied to both homogeneous and heterogeneous catalyst discovery, with a unified categorization and resource compilation.
Findings
AI accelerates catalyst discovery processes
Large language models enhance chemical reaction understanding
The survey identifies key challenges and future research directions
Abstract
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Topic Modeling
