Exploring Quality Criteria for the Computational Modeling of Heterogeneous Catalysts

In this millennium, computational modeling has become an essential tool to understand the performance of heterogeneous catalysts. In this concept study, it focuses on three different areas, namely theoretical methodology, model complexity, and comparison to experiment and highlight specific approaches and develop a hierarchy of simulation models. In general, higher quality approaches coincide with a significant increase in computational cost. Therefore, this focuses on one specific example, namely the conversion of methane to methanol over Cu-exchanged zeolites, and illustrates how a cost-benefit analysis can lead to a high-quality, cost-efficient model. This study believes that this work can serve as a guideline for students starting to work in electronic structure calculations, will help researchers to more carefully choose their modeling methodology, and might help experimental researchers to better judge computational work presented to them.

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Publication Details

Journal
ChemPhysChem
Published
2025-03-26
DOI
https://doi.org/10.1002/cphc.202401123
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Exploring Quality Criteria for the Computational Modeling of Heterogeneous Catalysts

Florian Göeltl
ChemPhysChem
Machine Learning in Materials Science
article

Exploring Quality Criteria for the Computational Modeling of Heterogeneous Catalysts

Florian Göeltl
article en

Abstract

In this millennium, computational modeling has become an essential tool to understand the performance of heterogeneous catalysts. In this concept study, it focuses on three different areas, namely theoretical methodology, model complexity, and comparison to experiment and highlight specific approaches and develop a hierarchy of simulation models. In general, higher quality approaches coincide with a significant increase in computational cost. Therefore, this focuses on one specific example, namely the conversion of methane to methanol over Cu-exchanged zeolites, and illustrates how a cost-benefit analysis can lead to a high-quality, cost-efficient model. This study believes that this work can serve as a guideline for students starting to work in electronic structure calculations, will help researchers to more carefully choose their modeling methodology, and might help experimental researchers to better judge computational work presented to them.

ChemPhysChemVol. 26(11)
University of Arizona (US)
Openalex Percentile: Top 96%
Machine Learning in Materials Science
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