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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Nature Portfolio
2021
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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) |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 |
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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 |
_version_ |
1718381894316326912 |