Полностью определенный катализ
This chapter provides an overview of the "totally defined catalysis" concept, which combines advanced analytical methods, computational modeling, and machine learning to describe in detail the structure and dynamics of catalytic systems under real catalytic conditions. Special attention is given to dynamic processes, including nanoparticle rearrangement, atomic migration, and phase transitions induced by reaction conditions. Modern approaches, such as in situ and operando methods that allow real-time observation of catalytic processes with atomic resolution, are reviewed. The key role of machine learning and computer vision in analyzing large amounts of data, automating the identification of atoms and nanoparticles, and predicting their behavior is shown. Examples of the application of these technologies to create catalysts with tunable properties, including single-atom alloys and "catalyst cocktail" type systems, are discussed. The integration of quantum-chemical modeling and artificial intelligence opens new possibilities for creating digital twins of catalytic systems. This chapter is aimed at researchers working in catalysis, machine learning, and nanotechnology, demonstrating how the combination of experimental and computational methods contributes to the understanding of catalytic processes and the development of high-performance materials. This interdisciplinary approach outlines a new horizon in organometallic chemistry, highlighting the emergence of nanoscale surface organometallic systems that bridge classical molecular chemistry with dynamic catalytic interfaces.
Reference: Adv. Organomet. Chem., 2025, 84, 255-285.
