The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design

doi:10.1016/j.cjche.2025.04.012

Chinese Journal of Chemical Engineering ›› 2025, Vol. 84 ›› Issue (8): 117-132.DOI: 10.1016/j.cjche.2025.04.012

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The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design

Zhi Ma¹, Peng Cui², Xu Wang², Lanyu Li¹, Haoxiang Xu¹, Adrian Fisher³, Daojian Cheng¹

1. State Key Laboratory of Organic-Inorganic Composites and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China;
2. Petrochemical Research Institute, PetroChina, Beijing 102206, China;
3. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK

Received:2025-01-15 Revised:2025-04-14 Accepted:2025-04-15 Online:2025-05-14 Published:2025-08-28
Contact: Lanyu Li,E-mail:lilanyu@buct.edu.cn;Daojian Cheng,E-mail:chengdj@mail.buct.edu.cn
Supported by:
This work is supported by the Special Project of National Natural Science Foundation (42341204) and the the National Natural Science Foundation of China (W2411009).

The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design

Zhi Ma¹, Peng Cui², Xu Wang², Lanyu Li¹, Haoxiang Xu¹, Adrian Fisher³, Daojian Cheng¹

1. State Key Laboratory of Organic-Inorganic Composites and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China;
2. Petrochemical Research Institute, PetroChina, Beijing 102206, China;
3. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK

通讯作者: Lanyu Li,E-mail:lilanyu@buct.edu.cn;Daojian Cheng,E-mail:chengdj@mail.buct.edu.cn
基金资助:
This work is supported by the Special Project of National Natural Science Foundation (42341204) and the the National Natural Science Foundation of China (W2411009).

Abstract

Abstract: The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.

Key words: Catalysis, Machine learning, High-throughput experiment, Catalyst, Optimization, Data-driven research

摘要： The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.

关键词: Catalysis, Machine learning, High-throughput experiment, Catalyst, Optimization, Data-driven research

Zhi Ma, Peng Cui, Xu Wang, Lanyu Li, Haoxiang Xu, Adrian Fisher, Daojian Cheng. The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 117-132.

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The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design

The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design

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