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
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.
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