Wang Rui, Liang Fei, Lin Zheshuai
University of Chinese Academy of Sciences, Beijing, 100190, China.
Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, 100190, China.
Sci Rep. 2020 Feb 26;10(1):3486. doi: 10.1038/s41598-020-60410-x.
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, LiCuPS with ultrawide bandgap and CuCdSnTe with giant SHG response, are identified by this model.
将高通量筛选与机器学习模型相结合是探索新型光电功能材料的一个快速发展的方向。在此，我们采用随机森林回归（RFR）模型来研究具有独特类金刚石（DL）结构的非线性光学晶体的二次谐波产生（SHG）系数。从无机晶体结构数据库（ICSD）中选取了61种DL结构，并使用包括带隙、电负性、基团体积和键柔性在内的四个独特描述符对二阶非线性进行建模和预测。结果表明，RFR模型已达到第一性原理计算精度，并对各种代表性的DL晶体给出了有效的预测。此外，该模型在探索具有优异中红外非线性光学性能的四元DL体系新型晶体材料方面显示出广阔的应用前景。通过该模型识别出了两种新型潜在的非线性光学晶体，具有超宽带隙的LiCuPS和具有巨大SHG响应的CuCdSnTe。
