Computer‐Aided Development of New Nonlinear Optical Materials
Hongshan Wang, Miriding Mutailipu, Zhihua Yang, Shilie Pan, Junjie Li
Abstract
Exploring new nonlinear optical (NLO) materials is an urgent need for advanced photoelectric technologies. However, the discovery of new materials with targeted properties is time-consuming, and involves various challenges by the traditional trial-and-error experiments. Recently, the theoretical prediction-guided structural design has been demonstrated as a feasible way for efficiently developing new NLO materials, and a large number of NLO candidates with excellent optical properties have been explored. To promote the development of high-performance NLO materials, this review provides a summary on the exploration of new NLO materials aided by computer, with a particular emphasis on the state-of-the-art research advances that including crystal structure predictions, optical & thermal property calculations, high-throughput screening of NLO materials with or without machine learning; and the progress achieved in the computer-assisted design and development of new deep ultraviolet (DUV), ultraviolet (UV), infrared (IR) NLO materials in various material systems: oxide, chalcogenide, nitride, and halide. Finally, the opportunities and forthcoming challenges in the fascinating field are discussed.