Integrative Approaches to Reveal Catalyst Dynamics: Bridging Operando Techniques, Theory, and Artificial Intelligence

Catalysts operate under complex conditions that require sophisticated approaches to understand their dynamics. This perspective outlines advances in experimental operando techniques, theoretical approaches, and machine learning (ML)-based data analysis to elucidate catalyst dynamics and improve the next-generation catalyst design. We first survey operando techniques, spanning electron microscopy, X-ray spectroscopy, and vibrational spectroscopy, that capture catalyst dynamics under operating conditions. We then discuss how operando observations integrate with and inform theoretical models, creating an iterative feedback loop between experiment and computation. Finally, we highlight how advanced data analysis, especially ML, enables the interpretation of high-dimensional operando data sets and can even inform catalyst design. Together, these synergetic approaches provide a unified framework for probing catalyst function and accelerating the rational design of efficient, durable catalytic systems for sustainable chemical manufacturing.