Division/Committee: [CATL] Division of Catalysis Science & Technology

The pressing demand for energy-efficient processes is driving research into the swift exploration of chemical space through accelerated catalyst discovery efforts. Recent advancements in both experimental and computational high-throughput methods hold the potential to revolutionize the manipulation of variables in catalytic processes, elevating and outpacing traditional approaches. Nevertheless, challenges endure in the catalysis field, spanning the hurdles of obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions, and navigating the complexity of computational models and the accuracy of their predictions. Herein, the integration of computational and experimental approaches remains a pivotal challenge. Active areas of research include an augmented use of machine learning for efficient catalyst screening, progress in high-throughput experimentation, data-driven methodologies employing comprehensive databases, in situ and in operando techniques for realistic observations, and collaborative efforts across disciplines. This symposium aims to cover cutting-edge high-throughput experimental and computational approaches, propelling accelerated catalyst discovery in both academia and industry.

Peter Margl

Appearances