Morán-González Lucía, Burnage Arron L, Nova Ainara, Balcells David
Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway.
Centre for Materials Science and Nanotechnology, Department of Chemistry, University of Oslo, 0315 Oslo, Norway.
ACS Catal. 2025 May 14;15(11):9089-9105. doi: 10.1021/acscatal.5c01202. eCollection 2025 Jun 6.
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.
人工智能（AI）正在改变化学领域的研究，包括过渡金属均相催化。在过去15年里，将人工智能与催化相结合的出版物数量呈指数级增长，反映出该策略在该领域的关注度和影响力。由于这是一门广泛的新兴学科，制定明确现有各种方法的指导方针至关重要。使用人工智能工具可执行任务的复杂性直接与其组件的性质相关，包括数据集、表示法、算法以及高通量实验和计算设施。随着这些工具的不断发展，其在催化领域的应用也取得了进展。最初，开发模型是为了预测反应机理的关键方面，旨在筛选候选催化剂。随后的研究纳入了实验数据以优化反应条件和产率。最近，基于深度学习方法的生成式人工智能实现了具有预定义目标特性的新型催化剂的逆向设计。虽然大多数研究依赖计算数据，但最近的进展改善了实验数据的获取，实现了人工智能驱动的自动化工作流程。本综述对所选研究进行了批判性概述，这些研究反映了人工智能在均相金属催化反应中应用的当前水平，同时也强调了未来的机遇和挑战。
