Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Symbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by expe...

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Main Authors: Baicheng Weng, Zhilong Song, Rilong Zhu, Qingyu Yan, Qingde Sun, Corey G. Grice, Yanfa Yan, Wan-Jian Yin
Format: Article
Language:English
Published: Nature Portfolio 2020-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-17263-9
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author Baicheng Weng
Zhilong Song
Rilong Zhu
Qingyu Yan
Qingde Sun
Corey G. Grice
Yanfa Yan
Wan-Jian Yin
author_facet Baicheng Weng
Zhilong Song
Rilong Zhu
Qingyu Yan
Qingde Sun
Corey G. Grice
Yanfa Yan
Wan-Jian Yin
author_sort Baicheng Weng
collection DOAJ
description Symbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by experimental validation.
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spelling doaj.art-676d3825e20a4a98abc38543327d92c62022-12-21T21:21:12ZengNature PortfolioNature Communications2041-17232020-07-011111810.1038/s41467-020-17263-9Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalystsBaicheng Weng0Zhilong Song1Rilong Zhu2Qingyu Yan3Qingde Sun4Corey G. Grice5Yanfa Yan6Wan-Jian Yin7Department of Physics & Astronomy, and Wright Center for Photovoltaics Innovation and Commercialization, The University of ToledoCollege of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow UniversityCollege of Chemistry and Chemical Engineering, Hunan UniversityCollege of Chemistry and Chemical Engineering, Hunan UniversityCollege of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow UniversityDepartment of Physics & Astronomy, and Wright Center for Photovoltaics Innovation and Commercialization, The University of ToledoDepartment of Physics & Astronomy, and Wright Center for Photovoltaics Innovation and Commercialization, The University of ToledoCollege of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow UniversitySymbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by experimental validation.https://doi.org/10.1038/s41467-020-17263-9
spellingShingle Baicheng Weng
Zhilong Song
Rilong Zhu
Qingyu Yan
Qingde Sun
Corey G. Grice
Yanfa Yan
Wan-Jian Yin
Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
Nature Communications
title Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
title_full Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
title_fullStr Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
title_full_unstemmed Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
title_short Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
title_sort simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
url https://doi.org/10.1038/s41467-020-17263-9
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