Dataset for High-Entropy Alloys Phases

Ronald Machaka, Glenda T. Motsi, Lerato M. Raganya, Precious M. Radingoana, Silethelwe Chikosha,
Machine learning-based prediction of phases in high-entropy alloys: A data article,
Data in Brief,
2021,
107346,
ISSN 2352-3409,
https://doi.org/10.1016/j.dib.2021.107346.
(https://www.sciencedirect.com/science/article/pii/S2352340921006302)
Abstract: ABSTRACT
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”, are 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.
Keywords: High entropy alloys; HEA microstructures; phases; machine learning; deep learning; material informatics

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The dataset contains microstructural phase experimental observations and metallurgy-specific features as introduced and reported in peer-reviewed research articles.

Secondary data (i.e. composition-specific features, alloy processing and post-processing parameters, and the resulting phases) were collected. Some typical empirical HEA design parameters were calculated using known methods. Data was processed using Excel and R, a language and environment for statistical computing, for purposes of visualization and data analysis.