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 ...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.展开更多
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 hostin...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.展开更多
基金Supported by the National Key Research and Development Program of China under Grant Nos 2016YFA0401000 and2017YFA0302901the National Basic Research Program of China under Grant No 2015CB921000+2 种基金the National Natural Science Foundation of China under Grant Nos 11574371,11774399 and 11774398the Beijing Natural Science Foundation(Z180008)the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No XDB28000000
文摘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.
基金Supported by the National Key Research and Development Program(2016YFA0300502,2017YFA0302901,2016YFA0300604 and 2016YFA0300501)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB28000000,XDB07020100and QYZDB-SSW-SLH043)the National Natural Science Foundation of China under Grant Nos 11421092,11574359,11674370,11774399,11474330 and U1732154
文摘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.
