Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance  

作　　者：Wei Xu Rui Wang Chunhai Hu Guilin Wen Junqi Cui Longjiang Zheng Zhen Sun Yungang Zhang Zhiguo Zhang 

机构地区：[1]School of Electrical Engineering,Yanshan University,Qinhuangdao,China [2]Nanjing NARI Water Resources and Hydropower Technology Company Limited,NARI Group Corporation,Nanjing,China [3]School of Mechanical Engineering,Yanshan University,Qinhuangdao,China [4]College of Science,Yanshan University,Qinhuangdao,China [5]School of Instrumentation Science and Engineering,Harbin Institute of Technology,Harbin,China

出　　处：《npj Computational Materials》2024年第1期1103-1115,共13页计算材料学(英文)

基　　金：supported by the National Natural Science Foundation of China(NSFC Nos.12204401,12332002,and 62175208).

摘　　要：Near-infrared(NIR)phosphors based on Cr3+doped garnets present great potential in the next generation of NIR light sources.Nevertheless,the huge searching space for the garnet composition makes the rapid discovery of NIR phosphors with high performance remain a great challenge for the scientific community.Herein,a generalizable machine learning(ML)strategy is designed to accelerate the exploration of innovative NIR phosphors via establishing the relationship between key parameters and emission peak wavelength(EPW).We propose a semi-supervised co-training model based on kernel ridge regression(KRR)and support vector regression(SVR),which successfully establishes an expanded dataset with unlabeled dataset(previously unidentified garnets),addressing the overfitting issue resulted from a small dataset and greatly improving the model generalization capability.

关 键 词：enable PERFORMANCE establishing 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]