Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

Abstract The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large materia...

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Autores principales:	Fenglin Yuan, Tim Mueller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/22132188e8d84c629304643fd524eb34
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spelling	oai:doaj.org-article:22132188e8d84c629304643fd524eb342021-12-02T15:04:58ZIdentifying models of dielectric breakdown strength from high-throughput data via genetic programming10.1038/s41598-017-17535-32045-2322https://doaj.org/article/22132188e8d84c629304643fd524eb342017-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-17535-3https://doaj.org/toc/2045-2322Abstract The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large material data sets to identify models that can rapidly predict a given property. In this paper, we demonstrate the use of genetic programming to generate simple models of dielectric breakdown based on 82 representative dielectric materials. We identified the band gap Eg and phonon cut-off frequency ωmax as the two most relevant features, and new classes of models featuring functions of Eg and ωmax were uncovered. The genetic programming approach was found to outperform other approaches for generating models, and we discuss some of the advantages of this approach.Fenglin YuanTim MuellerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fenglin Yuan Tim Mueller Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
description	Abstract The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large material data sets to identify models that can rapidly predict a given property. In this paper, we demonstrate the use of genetic programming to generate simple models of dielectric breakdown based on 82 representative dielectric materials. We identified the band gap Eg and phonon cut-off frequency ωmax as the two most relevant features, and new classes of models featuring functions of Eg and ωmax were uncovered. The genetic programming approach was found to outperform other approaches for generating models, and we discuss some of the advantages of this approach.
format	article
author	Fenglin Yuan Tim Mueller
author_facet	Fenglin Yuan Tim Mueller
author_sort	Fenglin Yuan
title	Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
title_short	Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
title_full	Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
title_fullStr	Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
title_full_unstemmed	Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
title_sort	identifying models of dielectric breakdown strength from high-throughput data via genetic programming
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/22132188e8d84c629304643fd524eb34
work_keys_str_mv	AT fenglinyuan identifyingmodelsofdielectricbreakdownstrengthfromhighthroughputdataviageneticprogramming AT timmueller identifyingmodelsofdielectricbreakdownstrengthfromhighthroughputdataviageneticprogramming
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Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

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