The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify phases and crystal structures of HEAs has gained considerable significance. In this study, we assembled a new collection of 1345 HEAs with varying compositions to predict phases. Within this collection, there were 705 sets of data that were utilized to predict the crystal structures with the help of thermodynamics and electronic configuration. Our study introduces a methodical framework i.e., the Pearson correlation coefficient that helps in selecting the strongly co-related features to increase the prediction accuracy. This study employed five distinct boosting algorithms to predict phases and crystal structures, offering an enhanced guideline for improving the accuracy of these predictions. Among all these algorithms, XGBoost gives the highest accuracy of prediction (94.05%) for phases and LightGBM gives the highest accuracy of prediction of crystal structure of the phases (90.07%). The quantification of the influence exerted by parameters on the model's accuracy was conducted and a new approach was made to elucidate the contribution of individual parameters in the process of phase prediction and crystal structure prediction.

翻译：高熵合金（HEAs）优异性能的根源在于其包含的多样相与晶体结构。在材料信息学领域中，应用机器学习（ML）技术对HEAs的相与晶体结构进行分类已具有显著重要性。本研究构建了包含1345种不同成分HEAs的新数据集用于相预测，其中705组数据借助热力学与电子构型参数进行晶体结构预测。本研究提出了一种系统性框架——皮尔逊相关系数，通过筛选强相关特征提升预测精度。研究采用五种不同的提升算法进行相与晶体结构预测，为提升预测准确性提供了优化指南。在所有算法中，XGBoost对相的预测准确率最高（94.05%），而LightGBM对相晶体结构的预测准确率最高（90.07%）。本研究量化了各参数对模型精度的影响，并提出阐明单个参数在相预测与晶体结构预测过程中贡献度的新方法。