A coming of age for many-body methods: Achieving consensus with experiments for CO on MgO Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.26434/chemrxiv-2023-h4czl-v2
The adsorption energy of a molecule onto the surface of a material underpins a wide array of applications, spanning heterogeneous catalysis, gas storage and many more. It is the key quantity where experimental measurements and theoretical calculations meet, with agreement being necessary for reliable predictions of reaction rates and mechanisms. The prototypical molecule-surface system is CO adsorbed on MgO, but despite intense scrutiny from theory and experiment, there is still no consensus on its adsorption energy. In particular, the large cost of accurate many-body methods makes reaching converged theoretical estimates difficult, generating a wide range of values. In this work, we address this challenge, leveraging the latest advances in diffusion Monte Carlo (DMC) and coupled cluster theory [CCSD(T)], to obtain accurate predictions for CO on MgO. These reliable theoretical estimates allow us to evaluate the inconsistencies in published temperature programmed desorption experiments, revealing that they arise from variations in employed pre-exponential factors. Utilizing this insight, we derive new experimental estimates of the (electronic) adsorption energy with a (more) precise pre-exponential factor. As a culmination of all this effort, we are able to reach consensus between multiple theoretical calculations and multiple experiments for the first time. In addition, we show that our recently developed cluster-based CCSD(T) approach provides a low cost route towards achieving accurate adsorption energies. This sets the stage for affordable and reliable theoretical predictions of reaction mechanisms and rates to guide the realization of new catalysts and gas storage materials.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2023-h4czl-v2
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64f7a5643fdae147fa921f18/original/a-coming-of-age-for-many-body-methods-achieving-consensus-with-experiments-for-co-on-mg-o.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 100
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386481931
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386481931Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2023-h4czl-v2Digital Object Identifier
- Title
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A coming of age for many-body methods: Achieving consensus with experiments for CO on MgOWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-06Full publication date if available
- Authors
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Benjamin X. Shi, Andrea Zen, Venkat Kapil, Péter R. Nagy, Andreas Grüneis, Angelos MichaelidesList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv-2023-h4czl-v2Publisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64f7a5643fdae147fa921f18/original/a-coming-of-age-for-many-body-methods-achieving-consensus-with-experiments-for-co-on-mg-o.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/64f7a5643fdae147fa921f18/original/a-coming-of-age-for-many-body-methods-achieving-consensus-with-experiments-for-co-on-mg-o.pdfDirect OA link when available
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Adsorption, Realization (probability), Computer science, Cluster (spacecraft), Range (aeronautics), Statistical physics, Monte Carlo method, Diffusion, Scrutiny, Exponential function, Desorption, Chemistry, Materials science, Physics, Thermodynamics, Physical chemistry, Mathematics, Statistics, Composite material, Programming language, Political science, Mathematical analysis, LawTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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