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 model shows promising applications to explore new crystal materials of quaternary DL system with superior mid-IR NLO performances. Two new potential NLO crystals, Li2CuPS4 with ultrawide bandgap and Cu2CdSnTe4 with giant SHG response, are identified by this model.