Integrative Approaches to Reveal Catalyst Dynamics: Bridging <i>Operando</i> Techniques, Theory, and Artificial Intelligence.

Abstract

Catalysts operate under complex conditions that require sophisticated approaches to understand their dynamics. This perspective outlines advances in experimental <i>operando</i> techniques, theoretical approaches, and machine learning (ML)-based data analysis to elucidate catalyst dynamics and improve the next-generation catalyst design. We first survey <i>operando</i> techniques, spanning electron microscopy, X-ray spectroscopy, and vibrational spectroscopy, that capture catalyst dynamics under operating conditions. We then discuss how <i>operando</i> 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 <i>operando</i> 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.