A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal predictio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-11-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-020-00447-8 |
| _version_ | 1818429725726474240 |
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| author | Osman Mamun Kirsten T. Winther Jacob R. Boes Thomas Bligaard |
| author_facet | Osman Mamun Kirsten T. Winther Jacob R. Boes Thomas Bligaard |
| author_sort | Osman Mamun |
| collection | DOAJ |
| description | Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org. |
| first_indexed | 2024-12-14T15:22:05Z |
| format | Article |
| id | doaj.art-49cb2d1a410e45efa84ac22648868d1b |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-12-14T15:22:05Z |
| publishDate | 2020-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-49cb2d1a410e45efa84ac22648868d1b2022-12-21T22:56:07ZengNature Portfolionpj Computational Materials2057-39602020-11-016111110.1038/s41524-020-00447-8A Bayesian framework for adsorption energy prediction on bimetallic alloy catalystsOsman Mamun0Kirsten T. Winther1Jacob R. Boes2Thomas Bligaard3SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford UniversitySUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford UniversitySUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford UniversitySUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator LaboratoryAbstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.https://doi.org/10.1038/s41524-020-00447-8 |
| spellingShingle | Osman Mamun Kirsten T. Winther Jacob R. Boes Thomas Bligaard A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts npj Computational Materials |
| title | A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts |
| title_full | A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts |
| title_fullStr | A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts |
| title_full_unstemmed | A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts |
| title_short | A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts |
| title_sort | bayesian framework for adsorption energy prediction on bimetallic alloy catalysts |
| url | https://doi.org/10.1038/s41524-020-00447-8 |
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