Automatic graph representation algorithm for heterogeneous catalysis Article Swipe
Zachary Gariepy
,
Zhiwen Chen
,
Isaac Tamblyn
,
Chandra Veer Singh
,
Conrard Giresse Tetsassi Feugmo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1063/5.0140487
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1063/5.0140487
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of machine learning-aided exploration, more than current model architectures and processing algorithms are required. New architectures such as graph neural networks have seen significant research investments recently. For heterogeneous catalysis, defining substrate intramolecular bonds and adsorbate/substrate intermolecular bonds is a time-consuming and challenging process. Before applying a model, dataset pre-processing, node/bond descriptor design, and specific model constraints have to be considered. In this work, a framework designed to solve these issues is presented in the form of an automatic graph representation algorithm (AGRA) tool to extract the local chemical environment of metallic surface adsorption sites. This tool is able to gather multiple adsorption geometry datasets composed of different systems and combine them into a single model. To show AGRA’s excellent transferability and reduced computational cost compared to other graph representation methods, it was applied to five different catalytic reaction datasets and benchmarked against the Open Catalyst Projects graph representation method. The two oxygen reduction reaction (ORR) datasets with O/OH adsorbates obtained 0.053 eV root-mean-square deviation (RMSD) when combined together, whereas the three carbon dioxide reduction reaction datasets with CHO/CO/COOH obtained an average performance of 0.088 eV RMSD. To further display the algorithm’s versatility and extrapolation ability, a model was trained on a subset combination of all five datasets with an RMSD of 0.105 eV. This universal model was then used to predict a wide range of adsorption energies and an entirely new ORR catalyst system, which was then verified through density functional theory calculations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0140487
- https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0140487/18023508/036103_1_5.0140487.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383198477
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4383198477Canonical identifier for this work in OpenAlex
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https://doi.org/10.1063/5.0140487Digital Object Identifier
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Automatic graph representation algorithm for heterogeneous catalysisWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-07-05Full publication date if available
- Authors
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Zachary Gariepy, Zhiwen Chen, Isaac Tamblyn, Chandra Veer Singh, Conrard Giresse Tetsassi FeugmoList of authors in order
- Landing page
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https://doi.org/10.1063/5.0140487Publisher landing page
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https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0140487/18023508/036103_1_5.0140487.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0140487/18023508/036103_1_5.0140487.pdfDirect OA link when available
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Computer science, Algorithm, Planarity testing, Graph, Theoretical computer science, Chemistry, CrystallographyTop concepts (fields/topics) attached by OpenAlex
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2025: 2, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3087505564, https://openalex.org/W2999482379, https://openalex.org/W3196126979, https://openalex.org/W3048912808, https://openalex.org/W3186879433, https://openalex.org/W2909236073, https://openalex.org/W2462003543, https://openalex.org/W3158986751, https://openalex.org/W2992601128, https://openalex.org/W3070174055, https://openalex.org/W3156633173, https://openalex.org/W2766856748, https://openalex.org/W2961429249, https://openalex.org/W3036167153, https://openalex.org/W2601081289, https://openalex.org/W2132022337, https://openalex.org/W2949095042, https://openalex.org/W3093999435, https://openalex.org/W2173183968, https://openalex.org/W3212512279, https://openalex.org/W4309646813, https://openalex.org/W2972052805, https://openalex.org/W3025104221, https://openalex.org/W3173478819 |
| referenced_works_count | 24 |
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