基于特征工程和机器学习的铝基高熵合金稳定性预测

doi:10.12066/j.issn.1007-2861.2381

上海大学学报(自然科学版) ›› 2022, Vol. 28 ›› Issue (3): 476-484.doi: 10.12066/j.issn.1007-2861.2381

基于特征工程和机器学习的铝基高熵合金稳定性预测

胡瑞¹, 刘庆¹, 张光捷¹, 李俊杰¹, 陈晓玉², 魏晓¹^,³, 戴东波¹()

1.上海大学计算工程与科学学院, 上海 200444
2.上海大学材料基因组工程研究院材料信息与数据科学中心, 上海 200444
3.之江实验室, 浙江杭州 311100

收稿日期:2022-02-25 出版日期:2022-06-30 发布日期:2022-05-27
通讯作者: 戴东波 E-mail:dbdai@shu.edu.cn
作者简介:戴东波(1977—), 男, 讲师, 博士, 研究方向为材料信息学、数据挖掘等. E-mail: dbdai@shu.edu.cn
基金资助:
国家重点研发计划资助项目(2018YFB0704400);云南省重大科技专项资助项目(202002AB080001-2);之江实验室科研攻关资助项目(2021PE0AC02)

Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning

HU Rui¹, LIU Qing¹, ZHANG Guangjie¹, LI Junjie¹, CHEN Xiaoyu², WEI Xiao¹^,³, DAI Dongbo¹()

1. School Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2. Centerof Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China
3. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China

Received:2022-02-25 Online:2022-06-30 Published:2022-05-27
Contact: DAI Dongbo E-mail:dbdai@shu.edu.cn

摘要/Abstract

摘要：

铝基复合材料具有众多优异的性能, 应用前景较好. 以简单稳定相的高熵合金可以作为增强颗粒来制备铝基复合材料, 其各方面力学性能都显著提升. 提出了一种基于结合了特征工程和机器学习的新方法来研究高熵合金相稳定性. 该方法利用特征工程筛选出影响目标属性的重要因素, 然后选择相应的回归方法预测相稳定性. 使用 50% 的数据集进行训练, 并在其余数据集上进行测试验证. 研究结果表明, 该方法在预测高熵合金的相稳定性方面具有较高的准确性($R^{2}$=0.994), 且能辅助找到影响相稳定性的关键因素.

关键词: 铝基复合材料, 高熵合金, 特征工程, 机器学习, 相稳定性预测

Abstract:

Aluminum matrix composites offer many excellent properties and wide application prospects. High entropy alloys with a simple and stable phase can be used as reinforcement to prepare aluminum matrix composites with significantly improved performance in all aspects. Herein, a new method based on feature engineering and machine learning is proposed to investigate the phase stability of high entropy alloys. This method uses feature engineering to determine the important factors affecting the target attributes, and then selects the corresponding regression method to predict the phase stability. A model on 50% of the datasets is trained and then the model is verified on other datasets. The results show that this method is highly accurate in predicting the phase stability of high entropy alloys ($R^2=0.994$). In addition, this method can be used to identify key factors affecting phase stability.

Key words: aluminum matrix composite, high entropy alloy, feature engineering, machine learning, phase stability prediction

中图分类号:

TB 331

胡瑞, 刘庆, 张光捷, 李俊杰, 陈晓玉, 魏晓, 戴东波. 基于特征工程和机器学习的铝基高熵合金稳定性预测[J]. 上海大学学报(自然科学版), 2022, 28(3): 476-484.

HU Rui, LIU Qing, ZHANG Guangjie, LI Junjie, CHEN Xiaoyu, WEI Xiao, DAI Dongbo. Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 476-484.

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基于特征工程和机器学习的铝基高熵合金稳定性预测

Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning

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