Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning

Abstract Accurately evaluating the adsorption ability of adsorbents for heavy metal ions (HMIs) and organic pollutants in water is critical for the design and preparation of emerging highly efficient adsorbents. However, predicting adsorption capabilities of adsorbents at arbitrary sites is challeng...

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Autores principales:	Zhilong Wang, Haikuo Zhang, Jiahao Ren, Xirong Lin, Tianli Han, Jinyun Liu, Jinjin Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/1214c93ce21c44aa8679947d7f0c4742
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spelling	oai:doaj.org-article:1214c93ce21c44aa8679947d7f0c47422021-12-02T13:24:35ZPredicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning10.1038/s41524-021-00494-92057-3960https://doaj.org/article/1214c93ce21c44aa8679947d7f0c47422021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00494-9https://doaj.org/toc/2057-3960Abstract Accurately evaluating the adsorption ability of adsorbents for heavy metal ions (HMIs) and organic pollutants in water is critical for the design and preparation of emerging highly efficient adsorbents. However, predicting adsorption capabilities of adsorbents at arbitrary sites is challenging, with currently unavailable measuring technology for active sites and the corresponding activities. Here, we present an efficient artificial intelligence (AI) approach to predict the adsorption ability of adsorbents at arbitrary sites, as a case study of three HMIs (Pb(II), Hg(II), and Cd(II)) adsorbed on the surface of a representative two-dimensional graphitic-C3N4. We apply the deep neural network and transfer learning to predict the adsorption capabilities of three HMIs at arbitrary sites, with the predicted results of Cd(II) > Hg(II) > Pb(II) and the root-mean-squared errors less than 0.1 eV. The proposed AI method has the same prediction accuracy as the ab initio DFT calculation, but is millions of times faster than the DFT to predict adsorption abilities at arbitrary sites and only requires one-tenth of datasets compared to training from scratch. We further verify the adsorption capacity of g-C3N4 towards HMIs experimentally and obtain results consistent with the AI prediction. It indicates that the presented approach is capable of evaluating the adsorption ability of adsorbents efficiently, and can be further extended to other interdisciplines and industries for the adsorption of harmful elements in aqueous solution.Zhilong WangHaikuo ZhangJiahao RenXirong LinTianli HanJinyun LiuJinjin LiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
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 Zhilong Wang Haikuo Zhang Jiahao Ren Xirong Lin Tianli Han Jinyun Liu Jinjin Li Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
description	Abstract Accurately evaluating the adsorption ability of adsorbents for heavy metal ions (HMIs) and organic pollutants in water is critical for the design and preparation of emerging highly efficient adsorbents. However, predicting adsorption capabilities of adsorbents at arbitrary sites is challenging, with currently unavailable measuring technology for active sites and the corresponding activities. Here, we present an efficient artificial intelligence (AI) approach to predict the adsorption ability of adsorbents at arbitrary sites, as a case study of three HMIs (Pb(II), Hg(II), and Cd(II)) adsorbed on the surface of a representative two-dimensional graphitic-C3N4. We apply the deep neural network and transfer learning to predict the adsorption capabilities of three HMIs at arbitrary sites, with the predicted results of Cd(II) > Hg(II) > Pb(II) and the root-mean-squared errors less than 0.1 eV. The proposed AI method has the same prediction accuracy as the ab initio DFT calculation, but is millions of times faster than the DFT to predict adsorption abilities at arbitrary sites and only requires one-tenth of datasets compared to training from scratch. We further verify the adsorption capacity of g-C3N4 towards HMIs experimentally and obtain results consistent with the AI prediction. It indicates that the presented approach is capable of evaluating the adsorption ability of adsorbents efficiently, and can be further extended to other interdisciplines and industries for the adsorption of harmful elements in aqueous solution.
format	article
author	Zhilong Wang Haikuo Zhang Jiahao Ren Xirong Lin Tianli Han Jinyun Liu Jinjin Li
author_facet	Zhilong Wang Haikuo Zhang Jiahao Ren Xirong Lin Tianli Han Jinyun Liu Jinjin Li
author_sort	Zhilong Wang
title	Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
title_short	Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
title_full	Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
title_fullStr	Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
title_full_unstemmed	Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
title_sort	predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1214c93ce21c44aa8679947d7f0c4742
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Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning

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