Data-driven prediction of diamond-like infrared nonlinear optical crystals with targeting performances.

ABSTRACT: Combining high-throughput screening and machine learning models is a rapidly developed direction for the exploration of novel optoelectronic functional materials. Here, we employ random forests regression (RFR) model to investigate the second harmonic generation (SHG) coefficients of nonlinear optical crystals with distinct diamond-like (DL) structures. 61 DL structures in Inorganic Crystallographic Structure Database (ICSD) are selected, and four distinctive descriptors, including band gap, electronegativity, group volume and bond flexibility, are used to model and predict second-order nonlinearity. It is demonstrated that the RFR model has reached the first-principles calculation accuracy, and gives validated predictions for a variety of representative DL crystals. Additionally, this mod

SUBMITTER: Wang R 

PROVIDER: S-EPMC7044425 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature