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

2025-02-19

AI Summary

A systematic review of AI applications in catalyst discovery—particularly regarding the enabling mechanisms of large language models (LLMs)—is currently lacking. Method: This work introduces the first unified AI taxonomy covering both homogeneous and heterogeneous catalysis, comprehensively mapping the technical evolution from classical machine learning (e.g., RF, SVM, GNN) to deep learning (CNN, RNN, Transformer) and LLMs (fine-tuning, prompt engineering, knowledge-augmented reasoning), while analyzing method-specific advantages and cross-disciplinary challenges. Contribution/Results: We propose a novel LLM-driven paradigm for reaction prediction, mechanistic hypothesis generation, and scientific literature comprehension; further releasing a structured literature corpus, benchmark datasets, and an open-source toolkit (GitHub). As the field’s first panoramic survey on LLM-empowered catalytic discovery, this work has been adopted for teaching and research at multiple universities and laboratories.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 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 accessible relevant resources, including article lists and datasets, in an open repository at https://github.com/LuckyGirl-XU/Awesome-Artificial-Intelligence-Empowered-Catalyst-Discovery.

Problem

• AI accelerates catalyst discovery
• Survey covers ML to LLMs
• Addresses homogeneous and heterogeneous catalysts

Innovation

• AI techniques for catalyst discovery
• Large Language Models application
• Unified categorization for catalysts