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Classification and Prediction of Skyrmion Material Based on Machine Learning

查看全文 作　　者：Dan [1]Liu;Zhixin [1]Liu;JinE [2]Zhang;Yinong [1]Yin;Jianfeng [1]Xi;Lichen [3]Wang;JieFu [3]Xiong;Ming [4]Zhang;Tongyun [5]Zhao;Jiaying [6]Jin;Fengxia [5]Hu;Jirong [5]Sun;Jun [7]Shen;Baogen [3,5]Shen 高影响力作者 机构地区：[1]Department of Physics,School of Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,P.R.China;[2]School of Integrated Circuit Science and Engineering,Beihang University,Beijing 100191,China;[3]Ningbo Institute of Materials,Technology&Engineering,Chinese Academy of Sciences,Zhejiang 315201,P.R.China;[4]School of Physics,Inner Mongolia University of Science and Technology,Baotou 014010,P.R.China;[5]State Key Laboratory of Magnetism,Institute of Physics,Chinese Academy of Sciences,Beijing 100190,P.R.China;[6]School of Materials Science and Engineering,Zhejiang University,Hangzhou 310027,P.R.China;[7]Key Laboratory of Cryogenics,Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Beijing 100190,P.R.China高影响力机构 出　　处：《Research》索引2023年第4期,共11页高影响力期刊 基　　金：This work was supported by the National Natural Science Foundation of China(grant nos.52001012,52088101,and 51925605);the National Key Research and Development Program of China(2021YFB3501202);the Beijing Natural Science Foundation(grant no.2214070);the National Key Research and Development Program of China(2021YFB3501504,2022YFB3505201,2020YFA0711502,and 2019YFA0704900);the National Natural Science Foundation of China(grant nos.92263202 and 51971240);the Heye Health Technology Chong Ming Project(HYCMP-2022002 and HYCMP-2022003);the Natural Science Foundation of Inner Mongolia Autonomous Region(2019MS05040);the Strategic Priority Research Program B(XDB33030200);the Key Program of the Chinese Academy of Sciences(CAS)。 摘　　要：The discovery and study of skyrmion materials play an important role in basic frontier physics research and future information technology.The database of 196 materials,including 64 skyrmions,was established and predicted based on machine learning.A variety of intrinsic features are classified to optimize the model,and more than a dozen methods had been used to estimate the existence of skyrmion in magnetic materials,such as support vector machines,k-nearest neighbor,and ensembles of trees.It is found that magnetic materials can be more accurately divided into skyrmion and non-skyrmion classes by using the classification of electronic layer.Note that the rare earths are the key elements affecting the production of skyrmion.The accuracy and reliability of random undersampling bagged trees were 87.5%and 0.89,respectively,which have the potential to build a reliable machine learning model from small data.The existence of skyrmions in LaBaMnO is predicted by the trained model and verified by micromagnetic theory and experiments. 关 键 词：FRONTIER earths verified
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