Improving Phase Stability of α-CsPbI through Combined Strain-Doping Engineering: Insights from First-Principles Calculations and Machine Learning.

Journal: ACS applied materials & interfaces
Published Date:

Abstract

The all-inorganic perovskite of cubic CsPbI (α-CsPbI) exhibited high thermal stability, owing to the absence of volatile organic cations. However, the poor phase stability at room temperature limits its practical applications. In this work, the phase transition mechanism of CsPbI from the cubic phase (α) to the tetragonal phase (β) was studied by first-principles calculations. The critical pathway defined as α-TS1-MS1 with an energy barrier of 42.36 meV/atom was identified. In order to improve the phase stability of α-CsPbI, a combined strategy of compressive strain (0-5%) and B-site doping (including Be, Mg, Ca, Sr, and Ba) was proposed to suppress the α-β phase transition. Through systematic calculations, seven candidate systems were obtained, which exhibit higher phase transition barriers and similar band gaps relative to those of α-CsPbI. Furthermore, key descriptors, such as strain, B-site doping concentration, ., are employed to train machine learning models for predicting the α-β phase transition barriers and band gaps of α-CsPbI under different control conditions. Among them, the Extra Trees Regression (ETR) model demonstrates the highest accuracy and predicts a series of efficient and stable candidate systems for experimental validation. These results provide theoretical insights for enhancing the phase stability of α-CsPbI and improving the performance of related optoelectronic devices.

Authors

  • Weitao Yan
    Department of Micro/Nano Electronics, Tianjin Key Laboratory of Efficient Utilization of Solar Energy, Engineering Research Center of Thin Film Optoelectronics Technology (Ministry of Education), Nankai University, Tianjin 300350, China.
  • Longcheng Liang
    Department of Micro/Nano Electronics, Tianjin Key Laboratory of Efficient Utilization of Solar Energy, Engineering Research Center of Thin Film Optoelectronics Technology (Ministry of Education), Nankai University, Tianjin 300350, China.
  • Yao Sun
    School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
  • Boyan Li
    School of Automation, Guangdong University of Technology, Guangzhou, People's Republic of China.
  • Ying Zhao
    Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Wen Yang
    Department of Pharmaceutical Analysis, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China; Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
  • Zhehao Qu
    China Institute of Atomic Energy, Reactor Engineering Technology Research Institute, Beijing 102413, China.
  • Li An
    School of Science, Hebei University of Technology, Tianjin 300401, People's Republic of China.
  • Feng Lu
    National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
  • Wei-Hua Wang
    Department of Micro/Nano Electronics, Tianjin Key Laboratory of Efficient Utilization of Solar Energy, Engineering Research Center of Thin Film Optoelectronics Technology (Ministry of Education), Nankai University, Tianjin 300350, China.

Keywords

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