Wang Zhilong, Han Yanqiang, Lin Xirong, Cai Junfei, Wu Sicheng, Li Jinjin
National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China.
Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China.
ACS Appl Mater Interfaces. 2022 Jan 12;14(1):717-725. doi: 10.1021/acsami.1c18477. Epub 2021 Dec 30.
Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼10 faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0-2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.
无铅双钙钛矿被视为单钙钛矿稳定且环保的光电器件替代材料，但可能存在间接带隙和较高有效质量，从而限制了其最大光伏效率。鉴于试错式实验和计算方法无法快速识别理想候选材料，我们提出了一种集成学习工作流程，用于从元素周期表中筛选所有合适的双钙钛矿，预测准确率高达92%，计算速度比从头计算快约10倍。从约23314种未探索的双钙钛矿中，我们成功识别出六种具有合适带隙（1.0 - 2.0 eV）的候选材料，其中两种具有直接带隙和低有效质量。它们都表现出良好的热稳定性，有望能够合成。所提出的机器学习工作流程极大地缩短了双钙钛矿的筛选周期，这将极大地推动光伏器件的开发和应用。
