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.     

From Claringbullite to a New Spin Liquid Candidate Cu_3Zn(OH)_6FCl

The search for quantum spin liquid(QSL) materials has attracted significant attention in the field of condensed matter physics in recent years, however so far only a handful of them are considered as candidates hosting QSL ground state. Owning to their geometrically frustrated structures, Kagome materials are ideal systems to realize QSL. We synthesize the kagome structured material claringbullite(Cu_4(OH)_6FCl) and then replace inter-layer Cu with Zn to form Cu_3Zn(OH)_6FCl. Comprehensive measurements reveal that doping Zn~(2+) ions transforms magnetically ordered Cu_4(OH)_6FCl into a non-magnetic QSL candidate Cu_3Zn(OH)_6FCl. Therefore,the successful syntheses of Cu_4(OH)_6FCl and Cu_3Zn(OH)_6FCl provide not only a new platform for the study of QSL but also a novel pathway of investigating the transition between QSL and magnetically ordered systems.