Predicting Surface Strain Effects on Adsorption Energy with Graph Neural Networks Article Swipe
Christopher C. Price
,
Akash Singh
,
Nathan C. Frey
,
Vivek B. Shenoy
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2022-kqnpk
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2022-kqnpk
Modifying the adsorption energies of reaction intermediates on different material surfaces can significantly improve heterogeneous catalysis by reducing energy barriers for intermediate elementary reaction steps. Surface strain can increase or decrease the adsorption energy depending on the surface composition, adsorbate composition, surface facet, and adsorbate site, breaking traditional scaling relationships which inhibit energy barrier alteration in reactions such as ammonia synthesis. We aim to generate a model that maps the adsorption energy response to a given input strain for a range of adsorbates and catalyst structures. After generating a training dataset of strained copper binary alloy catalyst + adsorbate complexes from the Open Catalyst Project and calculating the adsorption energy with first-principles calculations (dataset made available), we train a graph neural network to learn the relationship between catalyst + adsorbate structure, surface strain, and adsorption energy. The model successfully predicts the nature of the adsorption energy response for 85% of surface strains, outperforming simpler model baselines. Using the ammonia synthesis reaction as an example system, we identify Cu-S alloy catalysts as promising candidates for strain engineering since the majority of surface strain patterns raise the adsorption energy of the *NH intermediate. We find that the strain response of similar adsorbates on the same surface can greatly vary due to the competition between surface relaxation under strain and relaxation of the coordination environment. Our presented machine learning approach can be applied to additional datasets to identify target strain patterns that can reduce energy barriers in heterogeneous catalysis.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2022-kqnpk
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/62378a51d7562723041f69ce/original/predicting-surface-strain-effects-on-adsorption-energy-with-graph-neural-networks.pdf
- OA Status
- gold
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293102089
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Raw OpenAlex JSON
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https://openalex.org/W4293102089Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2022-kqnpkDigital Object Identifier
- Title
-
Predicting Surface Strain Effects on Adsorption Energy with Graph Neural NetworksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-03-21Full publication date if available
- Authors
-
Christopher C. Price, Akash Singh, Nathan C. Frey, Vivek B. ShenoyList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv-2022-kqnpkPublisher landing page
- PDF URL
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/62378a51d7562723041f69ce/original/predicting-surface-strain-effects-on-adsorption-energy-with-graph-neural-networks.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/62378a51d7562723041f69ce/original/predicting-surface-strain-effects-on-adsorption-energy-with-graph-neural-networks.pdfDirect OA link when available
- Concepts
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Adsorption, Catalysis, Strain (injury), Materials science, Chemical physics, Ammonia production, Strain energy, Chemical engineering, Chemistry, Physical chemistry, Thermodynamics, Physics, Organic chemistry, Finite element method, Internal medicine, Engineering, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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64Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 64 |
| abstract_inverted_index.+ | 97, 128 |
| abstract_inverted_index.a | 65, 74, 79, 88, 118 |
| abstract_inverted_index.We | 61, 191 |
| abstract_inverted_index.an | 162 |
| abstract_inverted_index.as | 58, 161, 170 |
| abstract_inverted_index.be | 228 |
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| abstract_inverted_index.we | 116, 165 |
| abstract_inverted_index.*NH | 189 |
| abstract_inverted_index.85% | 148 |
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| abstract_inverted_index.The | 136 |
| abstract_inverted_index.aim | 62 |
| abstract_inverted_index.and | 43, 83, 105, 133, 216 |
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| abstract_inverted_index.due | 207 |
| abstract_inverted_index.for | 20, 78, 147, 173 |
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| abstract_inverted_index.copper | 93 |
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| abstract_inverted_index.neural | 120 |
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| abstract_inverted_index.example | 163 |
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| abstract_inverted_index.barriers | 19, 242 |
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| abstract_inverted_index.catalyst | 84, 96, 127 |
| abstract_inverted_index.datasets | 232 |
| abstract_inverted_index.decrease | 30 |
| abstract_inverted_index.energies | 3 |
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| abstract_inverted_index.predicts | 139 |
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| abstract_inverted_index.strains, | 151 |
| abstract_inverted_index.surfaces | 10 |
| abstract_inverted_index.training | 89 |
| abstract_inverted_index.Modifying | 0 |
| abstract_inverted_index.adsorbate | 39, 44, 98, 129 |
| abstract_inverted_index.catalysis | 15 |
| abstract_inverted_index.catalysts | 169 |
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| abstract_inverted_index.adsorption | 2, 32, 70, 108, 134, 144, 185 |
| abstract_inverted_index.alteration | 54 |
| abstract_inverted_index.baselines. | 155 |
| abstract_inverted_index.candidates | 172 |
| abstract_inverted_index.catalysis. | 245 |
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| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.07885431 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |