MIIT Key Laboratory of Advanced Display Materials and Devices, School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China.
Phys Chem Chem Phys. 2023 Mar 29;25(13):9123-9130. doi: 10.1039/d2cp04244d.
In order to accelerate the application of quaternary optoelectronic materials in the field of luminescence, it is crucial to develop new quaternary semiconductor materials with excellent properties. However, faced with vast alternative quaternary semiconductors, traditional trial-and-error methods tend to be laborious and inefficient. Here, we combined machine learning (ML) with density functional theory (DFT) calculation to predict the bandgaps of 2180 quaternary semiconductors, most of which were undeveloped but environmentally friendly. The evaluation coefficient () of the model using a random forest algorithm was up to 0.93 in ML. Four novel quaternary semiconductors with direct bandgaps: AgInGaS, AgZnInS, AgZnSnS, and AgZnGaS, were selected from the ML model. Then their electronic structures and optical properties were further verified and studied by DFT calculations, which demonstrated that the four quaternary semiconductors had direct bandgaps, a small effective mass, and a large exciton binding energy and Stokes shift. Our calculation could significantly speed up the discovery of novel optoelectronic semiconductors and has a certain reference value for the study of luminescent materials and devices.
为了加速四元光电材料在发光领域的应用，开发具有优异性能的新型四元半导体材料至关重要。然而，面对大量的替代四元半导体，传统的试错方法往往既费力又低效。在这里，我们将机器学习（ML）与密度泛函理论（DFT）计算相结合，预测了 2180 种四元半导体的能带隙，其中大多数都是尚未开发但环保的。使用随机森林算法的模型评估系数（）在 ML 中高达 0.93。从 ML 模型中选择了四种具有直接能带隙的新型四元半导体：AgInGaS、AgZnInS、AgZnSnS 和 AgZnGaS。然后，通过 DFT 计算进一步验证和研究了它们的电子结构和光学性质，证明了这四种四元半导体具有直接能带隙、小有效质量以及大激子结合能和斯托克斯位移。我们的计算可以显著加快新型光电半导体的发现速度，对发光材料和器件的研究具有一定的参考价值。
