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 ------------------- 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.

作者为Ronald Machaka、Glenda T. Motsi、Lerato M. Raganya、Precious M. Radingoana、Silethelwe Chikosha，其研究论文《基于机器学习的高熵合金相预测：数据论文》发表于《Data in Brief》2021年，文章编号107346，ISSN 2352-3409，DOI链接为https://doi.org/10.1016/j.dib.2021.107346，可通过ScienceDirect链接https://www.sciencedirect.com/science/article/pii/S2352340921006302获取。

摘要：此前已有研究提出一套系统性框架，用于筛选最具决定性的预测特征组合，并解决与高熵合金（high-entropy alloy, HEA）相关的多分类相分类问题[1]。本数据论文即配套呈现该研究论文《基于机器学习的高熵合金相预测》所关联的数据集。本数据集对已发表实验报道的高熵合金微观组织进行了系统性整理与全面调研，收录了经同行评议研究论文中介绍并报道的微观结构相实验观测结果与冶金学专属特征。本数据集随本文以补充材料形式提供。
由于本数据集采集自经过同行评议的实验类学术论文，其数据可用于解析高熵合金的微观结构特征，助力优化高熵合金相组成，并在机器学习、材料信息学及其他相关领域发挥重要作用。

关键词：高熵合金；高熵合金微观组织；相；机器学习；深度学习；材料信息学

本数据集包含经同行评议研究论文中介绍并报道的微观结构相实验观测结果与冶金学专属特征。研究人员采集了二手数据（即成分专属特征、合金加工及后处理参数，以及最终生成的相组成），并通过已知方法计算了若干典型的高熵合金经验设计参数。本数据集采用Excel与统计计算语言及环境R进行数据处理，以实现可视化与数据分析。

搜集汇总

背景与挑战

背景概述

该数据集是一个关于高熵合金（HEA）微观结构相的综合数据集，包含从实验同行评审文章中收集的相观察和冶金特征数据，用于机器学习预测HEA的相分类。数据集支持材料信息学和优化HEA相的研究，适用于机器学习和冶金领域。