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.     

Can a Single－Wall Carbon Nanotube be an X－Ray Waveguide？

We study the transport efficiency for x-rays of single-wall carbon nanotubes (SWCNTs) in theory. Three effects，i.e. refraction, absorption and x-ray tunneling loss, are considered. Our calculation shows that the SWCNT cannot be an x-ray waveguide due to the large x-ray tunneling loss. If the tunneling loss can be reduced effectively, a nanotube could be a waveguide.     

Self—Diffusion Mechanisma of ASdatom on Al（001）,（011）and （111） Surfaces

Using the first-principle molecular dynamical calculations,we have studied the adatom self-diffusion mechanisms on fcc Al(001),(011) and (111) surfaces,On each surface,there are several mechanisms,among which there is one favour mechanism with the minimum barrier energy.The atomic exchange mechanism along the [100] direction on the (001) surface,the long bridge hopping mechanism along the [110] direction on the (011) surface,and the bridge hopping mechanism along the [112] directioin on the (111) surface are the favour mechanisms.The activation energy profiles for various self-diffusion mechanisms are studied in details.