Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.

Perovskite light-emitting diodes (PeLEDs) have showed significant progress in recent years; the external quantum efficiency (EQE) of electroluminescence in green and red regions has exceeded 20%, but the efficiency in blue lags far behind. Here, a large cation CH 3 CH 2 NH 2 + is added in PEA 2 (CsPbBr 3) 2 PbBr 4 perovskite to decrease the Pb-Br orbit coupling and increase the bandgap for blue emission. X-ray diffraction and nuclear magnetic resonance results confirmed that the EA has successfully replaced Cs + cations to form PEA 2 (Cs 1-x EA x PbBr 3) 2 PbBr 4. This method modulates the photoluminescence from the green region (508 nm) into blue (466 nm), and over 70% photoluminescence quantum yield in blue is obtained. In addition, the emission spectra is stable under light and thermal stress. With configuration of PeLEDs with 60% EABr, as high as 12.1% EQE of sky-blue electroluminescence located at 488 nm has been demonstrated, which will pave the way for the full color display for the PeLEDs.

Single-crystalline quasi-2D PbI2 nanosheets and quasi-1D nanowires, which showed different crystallographic symmetries, were controllably synthesized and utilized in flexible photodetectors with excellent mechanical stability and durability.