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The aim is to enhance computational modeling in catalysis by capturing realistic complexities and dynamic behaviors in industrial contexts.

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

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The aim is to enhance computational modeling in catalysis by capturing realistic complexities and dynamic behaviors in industrial contexts.
Discussed strategies for incorporating dynamic effects in simulation.
Highlighted the role of machine-learning interatomic potentials in bridging quantum and mesoscale modeling.
Proposed the design of high-throughput workflows to aid decision-making in catalyst development.
Advocated for treating catalysts as dynamic systems to improve accuracy in modeling.
Emphasized the importance of understanding transient reactive interfaces and evolving surface chemistry.
Outlined the necessity of computational frameworks that accommodate realistic operating conditions for better guidance in screening.

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Perspective discusses enhancing computational modeling for dynamic catalytic systems in industry, suggesting improved decision-making processes.

Melani et al. (2026) studied this question.

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Perspective discusses enhancing computational modeling for dynamic catalytic systems in industry, suggesting improved decision-making processes.