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...

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Autores principales:	Osman Mamun, Kirsten T. Winther, Jacob R. Boes, Thomas Bligaard
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/49cb2d1a410e45efa84ac22648868d1b
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spelling	oai:doaj.org-article:49cb2d1a410e45efa84ac22648868d1b2021-12-02T12:30:49ZA Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts10.1038/s41524-020-00447-82057-3960https://doaj.org/article/49cb2d1a410e45efa84ac22648868d1b2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00447-8https://doaj.org/toc/2057-3960Abstract 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.Osman MamunKirsten T. WintherJacob R. BoesThomas BligaardNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Osman Mamun Kirsten T. Winther Jacob R. Boes Thomas Bligaard A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
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.
format	article
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
title	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_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_sort	bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/49cb2d1a410e45efa84ac22648868d1b
work_keys_str_mv	AT osmanmamun abayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT kirstentwinther abayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT jacobrboes abayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT thomasbligaard abayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT osmanmamun bayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT kirstentwinther bayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT jacobrboes bayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts AT thomasbligaard bayesianframeworkforadsorptionenergypredictiononbimetallicalloycatalysts
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A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

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