Machine learning and evolutionary prediction of superhard B-C-N compounds

Abstract We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, a...

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Autores principales: Wei-Chih Chen, Joanna N. Schmidt, Da Yan, Yogesh K. Vohra, Cheng-Chien Chen
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Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/1dbbbb5676ce42ad84ccbf4a5d072afb
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spelling oai:doaj.org-article:1dbbbb5676ce42ad84ccbf4a5d072afb2021-12-02T17:03:49ZMachine learning and evolutionary prediction of superhard B-C-N compounds10.1038/s41524-021-00585-72057-3960https://doaj.org/article/1dbbbb5676ce42ad84ccbf4a5d072afb2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00585-7https://doaj.org/toc/2057-3960Abstract We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC10N, B4C5N3, and B2C3N exhibit dynamically stable phases with hardness values >40 GPa, which are superhard materials that potentially could be synthesized by low-temperature plasma methods.Wei-Chih ChenJoanna N. SchmidtDa YanYogesh K. VohraCheng-Chien ChenNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-8 (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
Wei-Chih Chen
Joanna N. Schmidt
Da Yan
Yogesh K. Vohra
Cheng-Chien Chen
Machine learning and evolutionary prediction of superhard B-C-N compounds
description Abstract We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC10N, B4C5N3, and B2C3N exhibit dynamically stable phases with hardness values >40 GPa, which are superhard materials that potentially could be synthesized by low-temperature plasma methods.
format article
author Wei-Chih Chen
Joanna N. Schmidt
Da Yan
Yogesh K. Vohra
Cheng-Chien Chen
author_facet Wei-Chih Chen
Joanna N. Schmidt
Da Yan
Yogesh K. Vohra
Cheng-Chien Chen
author_sort Wei-Chih Chen
title Machine learning and evolutionary prediction of superhard B-C-N compounds
title_short Machine learning and evolutionary prediction of superhard B-C-N compounds
title_full Machine learning and evolutionary prediction of superhard B-C-N compounds
title_fullStr Machine learning and evolutionary prediction of superhard B-C-N compounds
title_full_unstemmed Machine learning and evolutionary prediction of superhard B-C-N compounds
title_sort machine learning and evolutionary prediction of superhard b-c-n compounds
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/1dbbbb5676ce42ad84ccbf4a5d072afb
work_keys_str_mv AT weichihchen machinelearningandevolutionarypredictionofsuperhardbcncompounds
AT joannanschmidt machinelearningandevolutionarypredictionofsuperhardbcncompounds
AT dayan machinelearningandevolutionarypredictionofsuperhardbcncompounds
AT yogeshkvohra machinelearningandevolutionarypredictionofsuperhardbcncompounds
AT chengchienchen machinelearningandevolutionarypredictionofsuperhardbcncompounds
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