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Design of high bulk moduli high entropy alloys using machine learning

In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were tr...

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Autores principales: Kandavalli, Manjunadh, Agarwal, Abhishek, Poonia, Ansh, Kishor, Modalavalasa, Ayyagari, Kameswari Prasada Rao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665368/
https://www.ncbi.nlm.nih.gov/pubmed/37993607
http://dx.doi.org/10.1038/s41598-023-47181-x
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author Kandavalli, Manjunadh
Agarwal, Abhishek
Poonia, Ansh
Kishor, Modalavalasa
Ayyagari, Kameswari Prasada Rao
author_facet Kandavalli, Manjunadh
Agarwal, Abhishek
Poonia, Ansh
Kishor, Modalavalasa
Ayyagari, Kameswari Prasada Rao
author_sort Kandavalli, Manjunadh
collection PubMed
description In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were trained to classify the elemental composition as HEA or non-HEA. Among these models, Gradient Boosting Classifier (GBC) was found to be the most accurate, with a test accuracy of 78%. Further, six regression models were trained to predict the bulk modulus of HEAs, and the best results were obtained by LASSO Regression model with an R-square value of 0.98 and an adjusted R-Square value of 0.97 for the test data set. This work effectively bridges the gap in the discovery and property analysis of HEAs. By accelerating material discovery via providing alternate means for designing virtual alloy compositions having favourable bulk modulus for respective applications, this work opens new avenues of applications of HEAs.
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spelling pubmed-106653682023-11-22 Design of high bulk moduli high entropy alloys using machine learning Kandavalli, Manjunadh Agarwal, Abhishek Poonia, Ansh Kishor, Modalavalasa Ayyagari, Kameswari Prasada Rao Sci Rep Article In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing the composition of HEA to achieve enhanced bulk modulus values. A total of 12 ML algorithms were trained to classify the elemental composition as HEA or non-HEA. Among these models, Gradient Boosting Classifier (GBC) was found to be the most accurate, with a test accuracy of 78%. Further, six regression models were trained to predict the bulk modulus of HEAs, and the best results were obtained by LASSO Regression model with an R-square value of 0.98 and an adjusted R-Square value of 0.97 for the test data set. This work effectively bridges the gap in the discovery and property analysis of HEAs. By accelerating material discovery via providing alternate means for designing virtual alloy compositions having favourable bulk modulus for respective applications, this work opens new avenues of applications of HEAs. Nature Publishing Group UK 2023-11-22 /pmc/articles/PMC10665368/ /pubmed/37993607 http://dx.doi.org/10.1038/s41598-023-47181-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kandavalli, Manjunadh
Agarwal, Abhishek
Poonia, Ansh
Kishor, Modalavalasa
Ayyagari, Kameswari Prasada Rao
Design of high bulk moduli high entropy alloys using machine learning
title Design of high bulk moduli high entropy alloys using machine learning
title_full Design of high bulk moduli high entropy alloys using machine learning
title_fullStr Design of high bulk moduli high entropy alloys using machine learning
title_full_unstemmed Design of high bulk moduli high entropy alloys using machine learning
title_short Design of high bulk moduli high entropy alloys using machine learning
title_sort design of high bulk moduli high entropy alloys using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665368/
https://www.ncbi.nlm.nih.gov/pubmed/37993607
http://dx.doi.org/10.1038/s41598-023-47181-x
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