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The role of artificial intelligence in catalyst design and synthesis

The development of catalysts has long depended on trial-and-error methods, which are time-consuming and often yield inconsistent data. To improve the precision and efficiency of the catalyst design, it is imperative to transition to a data-driven, automated paradigm of catalyst synthesis.

In a study published in , a research group led by Prof. Deng Dehui from the Dalian Institute of Chemical Â鶹ÒùÔºics (DICP) of the Chinese Academy of Sciences, collaborating with Dr. Li Haobo's group from Nanyang Technological University, systematically reviewed the transformative role of artificial intelligence (AI) in the design and synthesis of heterogeneous catalysts, and outlined future directions for AI-driven innovations in this field.

Machine learning (ML) was highlighted as a powerful tool for predicting catalyst structure-property relationships, optimizing synthesis conditions, and enabling high-throughput automated calculations and experiments. By identifying key performance descriptors, it reduced reliance on resource-intensive theoretical calculations such as density functional theory, accelerating the catalyst discovery process.

Advanced techniques such as active learning and generative models further enhance the design efficiency by prioritizing critical experiments and proposing novel catalyst candidates.

A central focus was the development of AI-powered closed-loop systems that integrate automated synthesis, characterization, and optimization. These systems improved data quality, minimized human error, and ensured reproducibility across the entire catalyst development cycle.

The current challenges were pointed out, which include the limited generalizability of AI models across diverse catalytic systems, the difficulty of integrating multidisciplinary datasets, and the need for better anomaly detection in automated workflows.

Researchers proposed technological roadmaps emphasizing cross-institutional data sharing and adaptive AI frameworks.

"This study provides a blueprint for transitioning catalysis research toward fully automated and intelligent paradigms, unlocking the efficiency in catalyst development," said Prof. Deng.