Taking Care of Complexity: A Pragmatic View on Computational Modeling in Catalysis and Materials Science

Computational modeling is a central tool in material science, yet its industrial relevance requires going beyond idealized reaction pathways on pristine surfaces. Real catalysts operate under harsh conditions, where local chemistry is tightly coupled to macroscopic reactor environments. The key challenge for computational catalysis is therefore not simply to perform larger or longer simulations, but to ensure that these calculations capture realistic complexity without compromising quantum-chemical accuracy. In this Perspective, we argue for a transition from static representations to an operando view of catalytic materials in sustainable technologies. We discuss strategies to incorporate dynamic effects, the role of machine-learning interatomic potentials in bridging quantum and mesoscale descriptions, and the design of high-throughput workflows that genuinely support decision-making. Our central message is that catalysts should no longer be treated as rigid structures, but as dynamic, adaptive systems characterized by thermal fluctuations, transient reactive interfaces, and evolving surface chemistry. From an industrial standpoint, success lies not in identifying a single minimum-energy pathway, but in developing robust computational frameworks capable of guiding screening and process decisions under realistic operating conditions.