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

Data in Brief

Lerato M. Raganya¹  Glenda T. Motsi²  Ronald Machaka³  Precious M. Radingoana³  Silethelwe Chikosha³ 

[1] Corresponding author.;School of Mining, Metallurgy and Chemical Engineering, University of Johannesburg, Johannesburg, South Africa;Advanced Materials Manufacturing, Manufacturing Cluster, Council for Scientific and Industrial Research, P.O. Box 395, Pretoria, 0001, South Africa;

关键词: High entropy alloys;    HEA microstructures;    Phases;    Machine learning;    Deep learning;    Material informatics;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown