Accurate Simulations of the Reaction of H2 on a Curved Pt Crystal through Machine Learning-Reference-Cited by-同舟云学术

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Accurate Simulations of the Reaction of H2 on a Curved Pt Crystal through Machine Learning

Published:2021-12-17 Issue:51 Volume:12 Page:12157-12164
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Gerrits Nick¹²^ORCID

Affiliation:

1. Leiden Institute of Chemistry, Leiden University, Gorlaeus Laboratories, P.O. Box 9502, 2300 RA Leiden, The Netherlands

2. Research Group PLASMANT, Department of Chemistry, University of Antwerp, Universiteitsplein 1, BE-2610 Wilrijk, Antwerp, Belgium

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.1c03395

Reference68 articles.

1. Elementary Steps in Heterogeneous Catalysis

2. The evolution of model catalytic systems; studies of structure, bonding and dynamics from single crystal metal surfaces to nanoparticles, and from low pressure (<10−3Torr) to high pressure (>10−3Torr) to liquid interfaces

3. First-principles kinetic modeling in heterogeneous catalysis: an industrial perspective on best-practice, gaps and needs

4. Degree of rate control approach to computational catalyst screening

5. Defect Chemistry in Heterogeneous Catalysis: Recognition, Understanding, and Utilization

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2. Accurate Reaction Probabilities for Translational Energies on Both Sides of the Barrier of Dissociative Chemisorption on Metal Surfaces;The Journal of Physical Chemistry Letters;2024-02-28

3. SBH17: Benchmark Database of Barrier Heights for Dissociative Chemisorption on Transition Metal Surfaces;Journal of Chemical Theory and Computation;2022-12-19

4. A high-dimensional neural network potential for molecular dynamics simulations of condensed phase nickel and phase transitions;Molecular Simulation;2022-12-15

5. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization;WIREs Computational Molecular Science;2022-11-16

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