In general, and from an AI perspective, catalysis is a hard problem requiring the optimization of many target properties. In this framework, the rich chemistry of the transition metals adds further challenges. Whereas discriminative models are challenged by the need for predicting multiple properties, generative models struggle to produce chemically valid output, in line with the complexity of the metal-ligand bonds and other effects beyond the first coordination sphere. For these reasons, the application of machine learning to homogeneous catalysis has evolved at a lower pace compared to other fields. The Hylleraas Centre published some of the first work done in this field back in 2020 and, since then, the amount of publications has increased by a factor of five. In this work, we reviewed the field, highlighting the most important achievements and the exciting problems that remain to be solved.
Abstract
Artificial intelligence (AI) is transforming research in chemistry, including homogeneous catalysis with transition metals. Over the past 15 years, the number of publications combining AI with catalysis has increased exponentially, reflecting the interest and strength of this strategy in the field. Since this is a broad emerging discipline, it is essential to establish guidelines that clarify the diverse approaches already available. The complexity of the tasks that can be carried out with AI tools is directly linked to the nature of their components, including datasets, representations, algorithms, and high-throughput experimental and computational facilities. In parallel to the evolution of these tools, applications to catalysis have also advanced. Initially, models were developed to predict key aspects of the reaction mechanism, aiming at screening catalyst candidates. Subsequent studies have incorporated experimental data to optimize reaction conditions and yields. More recently, generative AI based on deep learning methods has enabled the inverse design of novel catalysts with predefined target properties. While most studies rely on computational data, recent advancements have improved the acquisition of experimental data, enabling AI-driven automated workflows. This Perspective gives a critical overview on selected studies that reflect the state of the art in the application of AI to homogeneous metal-catalyzed reactions, also highlighting future opportunities and challenges.
Reference
Morán-González, L.; Burnage, A. L.; Nova, A.; Balcells, D. AI Approaches to Homogeneous Catalysis with Transition Metal Complexes ACS Catal. 2025, 15, ASAP.
First published online: 14 May 2025.