Machine learning-based prediction of phases in high-entropy alloys: A data article

Machaka, RonaldMotsi, Glenda TRaganya, Mampai LRadingoana, Precious MChikosha, Silethelwe2023-03-062023-03-062021-10Machaka, R., Motsi, G.T., Raganya, M.L., Radingoana, P.M. & Chikosha, S. 2021. Machine learning-based prediction of phases in high-entropy alloys: A data article. Data in Brief, 38. http://hdl.handle.net/10204/126452352-3409https://doi.org/10.1016/j.dib.2021.107346http://hdl.handle.net/10204/12645A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that research paper, titled “Machine learning-based prediction of phases in high-entropy alloys”, is presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures. It contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. The dataset is provided with this article as a supplementary file. Since the dataset was collected from experimental peer-reviewed articles, these data can provide insights into the microstructural characteristics of HEAs, can be used to improve the optimization HEA phases, and have an important role in machine learning, material informatics, as well as in other fields.FulltextenAttribution-NonCommercial-NoDerivs 3.0 United StatesDeep learningHEA microstructuresHigh entropy alloysMachine learningMaterial informaticsMachine learning-based prediction of phases in high-entropy alloys: A data articleArticleMachaka, R., Motsi, G. T., Raganya, M. L., Radingoana, P. M., & Chikosha, S. (2021). Machine learning-based prediction of phases in high-entropy alloys: A data article. Data in Brief, 38, http://hdl.handle.net/10204/12645Machaka, Ronald, Glenda T Motsi, Mampai L Raganya, Precious M Radingoana, and Silethelwe Chikosha "Machine learning-based prediction of phases in high-entropy alloys: A data article." Data in Brief, 38 (2021) http://hdl.handle.net/10204/12645Machaka R, Motsi GT, Raganya ML, Radingoana PM, Chikosha S. Machine learning-based prediction of phases in high-entropy alloys: A data article. Data in Brief, 38. 2021; http://hdl.handle.net/10204/12645.TY - Article AU - Machaka, Ronald AU - Motsi, Glenda T AU - Raganya, Mampai L AU - Radingoana, Precious M AU - Chikosha, Silethelwe AB - A systematic framework for choosing the most determinant combination of predictor features and solving the multiclass phase classification problem associated with high-entropy alloy (HEA) was recently proposed [1]. The data associated with that research paper, titled “Machine learning-based prediction of phases in high-entropy alloys”, is presented in this data article. This dataset is a systematic documentation and comprehensive survey of experimentally reported HEA microstructures. It contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles. The dataset is provided with this article as a supplementary file. Since the dataset was collected from experimental peer-reviewed articles, these data can provide insights into the microstructural characteristics of HEAs, can be used to improve the optimization HEA phases, and have an important role in machine learning, material informatics, as well as in other fields. DA - 2021-10 DB - ResearchSpace DP - CSIR J1 - Data in Brief, 38 KW - Deep learning KW - HEA microstructures KW - High entropy alloys KW - Machine learning KW - Material informatics LK - https://researchspace.csir.co.za PY - 2021 SM - 2352-3409 T1 - Machine learning-based prediction of phases in high-entropy alloys: A data article TI - Machine learning-based prediction of phases in high-entropy alloys: A data article UR - http://hdl.handle.net/10204/12645 ER -25475