Chin. Phys. Lett.  2019, Vol. 36 Issue (6): 068101    DOI: 10.1088/0256-307X/36/6/068101
Machine Learning to Instruct Single Crystal Growth by Flux Method
Tang-Shi Yao1,2†, Cen-Yao Tang1,2†, Meng Yang1,2†, Ke-Jia Zhu1,2, Da-Yu Yan1,2, Chang-Jiang Yi1,2, Zi-Li Feng1,2, He-Chang Lei4, Cheng-He Li4, Le Wang1,2, Lei Wang1**, You-Guo Shi1,2**, Yu-Jie Sun1,3,5**, Hong Ding1,3,5
1Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190
2University of Chinese Academy of Sciences, Beijing 100049
3CAS Centre for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100049
4Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices, Renmin University, Beijing 100872
5Songshan Lake Materials Laboratory, Dongguan 523808
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Tang-Shi Yao, Cen-Yao Tang, Meng Yang et al  2019 Chin. Phys. Lett. 36 068101
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Abstract Growth of high-quality single crystals is of great significance for research of condensed matter physics. The exploration of suitable growing conditions for single crystals is expensive and time-consuming, especially for ternary compounds because of the lack of ternary phase diagram. Here we use machine learning (ML) trained on our experimental data to predict and instruct the growth. Four kinds of ML methods, including support vector machine (SVM), decision tree, random forest and gradient boosting decision tree, are adopted. The SVM method is relatively stable and works well, with an accuracy of 81% in predicting experimental results. By comparison, the accuracy of laboratory reaches 36%. The decision tree model is also used to reveal which features will take critical roles in growing processes.
Received: 30 April 2019      Published: 12 May 2019
PACS:  81.10.-h (Methods of crystal growth; physics and chemistry of crystal growth, crystal morphology, and orientation)  
  61.50.Ah (Theory of crystal structure, crystal symmetry; calculations and modeling)  
  81.05.-t (Specific materials: fabrication, treatment, testing, and analysis)  
  89.20.Ff (Computer science and technology)  
Fund: Supported by the National Key Research and Development Program of China under Grant Nos 2016YFA0401000 and 2017YFA0302901, the National Basic Research Program of China under Grant No 2015CB921000, the National Natural Science Foundation of China under Grant Nos 11574371, 11774399 and 11774398, the Beijing Natural Science Foundation (Z180008), and the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No XDB28000000.
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Tang-Shi Yao
Cen-Yao Tang
Meng Yang
Ke-Jia Zhu
Da-Yu Yan
Chang-Jiang Yi
Zi-Li Feng
He-Chang Lei
Cheng-He Li
Le Wang
Lei Wang
You-Guo Shi
Yu-Jie Sun
Hong Ding
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