Machine Learning to Instruct Single Crystal Growth by Flux Method

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.     

拓扑半金属材料的单晶生长研究进展

拓扑半金属已经成为凝聚态物理研究的一个热点领域,这类材料的单晶生长是研究其物理性质的基础.目前,对于拓扑材料的研究已经形成了以理论计算为指引,对潜在的拓扑材料进行单晶制备,并结合物性测量对理论预言加以验证的科研合作方式.在这种科研团队合作中,单晶生长起衔接作用.本文介绍了近年来拓扑半金属材料单晶生长方法,涵盖了拓扑Dirac半金属、Weyl半金属、Node-Line半金属以及其他打破常规分类的拓扑绝缘体及拓扑半金属材料等,并针对各个材料,详细总结了其生长方法.