Semiconductors grown by the solution-processed method have shown low-cost,facile fabrication process and comparable performance.However,there are many reasons why it is difficult to achieve high quality films.For example,lattice constant mismatch is one of the problems when photovoltaic devices made of organ metallic perovskites.In this work,MAPbBrMA=CH3NH3^+perovskites single crystals grown on the surface of MAPbBr2.5 CI0.5 perovskites single crystals via liquid epitaxial growth method is demonstrated.It is found that when the lattice constants of the two perovskite single crystals are matched,another crystal can be grown on the surface of one crystal by epitaxial growth.The whole epitaxy growth process does not require high heating temperature and long heating time.X-ray diffraction method is used to prove the lattice plane of the substrate and the epitaxial grown layer.A scanning electron microscope is used to measure the thickness of the epitaxial layer.Compared with perovskite-based photodetectors without epitaxial growth layer,perovskite-based photodetectors with epitaxial growth layer have lower dark current density and higher optical responsibility. 相似文献
Advanced machine learning(ML)approaches such as transfer learning have seldom been applied to approximate quantum many-body systems.Here we demonstrate that a simple recurrent unit(SRU)based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional(ID)Ising model with simultaneous transverse and parallel magnetic fields,as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions.At a cost of constant computational complexity,a larger many-body state evolution is predicted in an autoregressive way from just one initial state,without any guidance or knowledge of any Hamiltonian.Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system. 相似文献