Issue 31, 2024

Automation and machine learning augmented by large language models in a catalysis study

Abstract

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.

Graphical abstract: Automation and machine learning augmented by large language models in a catalysis study

Article information

Article type
Review Article
Submitted
31 Dec 2023
Accepted
21 Jun 2024
First published
26 Jun 2024
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2024,15, 12200-12233

Automation and machine learning augmented by large language models in a catalysis study

Y. Su, X. Wang, Y. Ye, Y. Xie, Y. Xu, Y. Jiang and C. Wang, Chem. Sci., 2024, 15, 12200 DOI: 10.1039/D3SC07012C

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