基于材料组分信息的高居里温度铁磁材料预测  

Prediction of ferromagnetic materials with high Curie temperature based on material composition information

作　　者：孙敬淇 吴绪才 阙志雄 张卫兵[1] Sun Jing-Qi;Wu Xu-Cai;Que Zhi-Xiong;Zhang Wei-Bing(Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering,School of Physics&Electronic Science,Changsha University of Science and Technology,Changsha 410004,China)

机构地区：[1]长沙理工大学物理与电子科学学院,柔性电子材料基因工程湖南省重点实验室,长沙410004

出　　处：《物理学报》2023年第18期183-189,共7页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:11874092);霍英东教育基金会第十六届高等院校青年教师基金(批准号:161005);湖南省杰出青年科学基金(批准号:2021JJ10039)资助的课题。

摘　　要：寻找具有高居里温度的铁磁材料是凝聚态物理的热点问题.本文建立了有效的基于材料组分信息的居里温度机器学习模型,并预测了多种高居里温度铁磁材料.基于收集到的1568个铁磁材料数据,并以铁磁材料的组分信息作为描述符,通过超参数优化和十折交叉验证,构建了支持向量回归、核岭回归、随机森林及极端随机树四种高效的机器学习模型.这其中,极端随机树模型具有最好的预测性能,其交叉验证R^(2)评分可达81.48%.同时,还应用极端随机树模型对Materials Project数据库36949种铁磁材料进行了预测,发现了338个居里温度大于600 K的铁磁材料.本文提出的方法可以为获取具有高居里温度的铁磁材料提供有价值的帮助,加快铁磁材料设计的过程.The search for ferromagnetic materials with high Curie temperature(T_(c))is a hot issue in condensed matter physics.In this work,an effective machine learning model of Curie temperature based on material component information is established to predict a variety of ferromagnetic materials with high Curie temperature.Based on the collected data of 1568 ferromagnetic materials,and taking the component information of ferromagnetic materials as descriptors,in this work four efficient machine learning models are constructed,namely support vector regression,kernel ridge regression,random forest and extremely randomized trees,through hyperparameter optimization and ten-break cross-validation.Of them,extremely randomized tree model has the best prediction performance,and its cross-validation R^(2)score can reach 81.48%.At the same time,the extremely randomized tree model is also used to predict 36949 materials in the materials project database,and 338 ferromagnetic materials with Tc greater than 600 K are found in this work.The method proposed in this paper can help obtain ferromagnetic materials with high Curie temperature and accelerate the process of ferromagnetic material design.

关 键 词：机器学习 铁磁材料 材料组分 居里温度 

分 类 号：TM271[一般工业技术—材料科学与工程]