ArticleProceedings of the National Academy of Sciences of the United States of America2023
The role of dynamics in heterogeneous catalysis: Surface diffusivity and N
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 74 citations in OpenAlex.
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- The role of the NNature communications · 2026Article
- Fluctuation-Mediated Model for Hydrogen-Inhibited NThe journal of physical chemistry letters · 2026Article
- Determination of coverage-dependent surface reaction rates using λ-dynamics: Application to molecular desorption.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Sliding friction over individual aromatic bonds correlates with bond order.Nature communications · 2026Article
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- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- A Machine Learning Interatomic Potential Data Set and Model for Catalysis with Local Fine-Tuning to Chemical Accuracy.JACS Au · 2025Article
- Electrochemical potential-driven water dynamics control CONature communications · 2025Article
- No Time for Nitrides: How Cobalt Alloying Promotes Iron Catalysts for Ammonia Decomposition.ACS catalysis · 2025Article
- Weighted active space protocol for multireference machine-learned potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Decoding technical multi-promoted ammonia synthesis catalysts.Nature communications · 2025Article
- Free-Energy Landscapes and Surface Dynamics in Methane Activation on Ni(511) via Machine Learning and Enhanced Sampling.ACS catalysis · 2025Article
- Machine learning-driven molecular dynamics unveils a bulk phase transformation driving ammonia synthesis on barium hydride.Nature communications · 2025Article
- Fast and Fourier features for transfer learning of interatomic potentials.npj computational materials · 2025Article
- Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis.Precision chemistry · 2024Review
- Metal-support interactions in metal oxide-supported atomic, cluster, and nanoparticle catalysis.Chemical Society reviews · 2024Review
- Toward an ab Initio Description of Adsorbate Surface Dynamics.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamics has long been recognized to play an important role in heterogeneous catalytic processes. However, until recently, it has been impossible to study their dynamical behavior at industry-relevant temperatures. Using a combination of machine learning potentials and advanced simulation techniques, we investigate the cleavage of the N[Formula: see text] triple bond on the Fe(111) surface. We find that at low temperatures our results agree with the well-established picture. However, if we increase the temperature to reach operando conditions, the surface undergoes a global dynamical change and the step structure of the Fe(111) surface is destabilized. The catalytic sites, traditionally associated with this surface, appear and disappear continuously. Our simulations illuminate the danger of extrapolating low-temperature results to operando conditions and indicate that the catalytic activity can only be inferred from calculations that take dynamics fully into account. More than that, they show that it is the transition to this highly fluctuating interfacial environment that drives the catalytic process.
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