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Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

Author

Listed:
  • Shuaihua Lu

    (Southeast University)

  • Qionghua Zhou

    (Southeast University)

  • Yixin Ouyang

    (Southeast University)

  • Yilv Guo

    (Southeast University)

  • Qiang Li

    (Southeast University)

  • Jinlan Wang

    (Southeast University)

Abstract

Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.

Suggested Citation

  • Shuaihua Lu & Qionghua Zhou & Yixin Ouyang & Yilv Guo & Qiang Li & Jinlan Wang, 2018. "Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05761-w
    DOI: 10.1038/s41467-018-05761-w
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    Cited by:

    1. Wan, Shuaibin & Liang, Xiongwei & Jiang, Haoran & Sun, Jing & Djilali, Ned & Zhao, Tianshou, 2021. "A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries," Applied Energy, Elsevier, vol. 298(C).
    2. Yilei Wu & Chang-Feng Wang & Ming-Gang Ju & Qiangqiang Jia & Qionghua Zhou & Shuaihua Lu & Xinying Gao & Yi Zhang & Jinlan Wang, 2024. "Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Li, Jing & Yu, Qian, 2024. "Scientists’ disciplinary characteristics and collaboration behaviour under the convergence paradigm: A multilevel network perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    4. Saxena, Shatakshi & Johnson, Michael & Dixit, Fuhar & Zimmermann, Karl & Chaudhuri, Shreya & Kaka, Fiyanshu & Kandasubramanian, Balasubramanian, 2023. "Thinking green with 2-D and 3-D MXenes: Environment friendly synthesis and industrial scale applications and global impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    5. Xinyu Chen & Shuaihua Lu & Qian Chen & Qionghua Zhou & Jinlan Wang, 2024. "From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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