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Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory

Author

Listed:
  • Yilei Wu

    (Southeast University)

  • Chang-Feng Wang

    (Zhejiang Normal University)

  • Ming-Gang Ju

    (Southeast University)

  • Qiangqiang Jia

    (Zhejiang Normal University)

  • Qionghua Zhou

    (Southeast University)

  • Shuaihua Lu

    (Southeast University)

  • Xinying Gao

    (Southeast University)

  • Yi Zhang

    (Zhejiang Normal University)

  • Jinlan Wang

    (Southeast University
    Suzhou Laboratory)

Abstract

The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44236-5
    DOI: 10.1038/s41467-023-44236-5
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    1. Jiadong Zhou & Junhao Lin & Xiangwei Huang & Yao Zhou & Yu Chen & Juan Xia & Hong Wang & Yu Xie & Huimei Yu & Jincheng Lei & Di Wu & Fucai Liu & Qundong Fu & Qingsheng Zeng & Chuang-Han Hsu & Changli , 2018. "A library of atomically thin metal chalcogenides," Nature, Nature, vol. 556(7701), pages 355-359, April.
    2. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    3. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
    4. 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.
    5. Yunxia Zhang & Yucheng Liu & Zhuo Xu & Haochen Ye & Zhou Yang & Jiaxue You & Ming Liu & Yihui He & Mercouri G. Kanatzidis & Shengzhong (Frank) Liu, 2020. "Publisher Correction: Nucleation-controlled growth of superior lead-free perovskite Cs3Bi2I9 single-crystals for high-performance X-ray detection," Nature Communications, Nature, vol. 11(1), pages 1-2, December.
    6. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
    7. Christopher Sutton & Mario Boley & Luca M. Ghiringhelli & Matthias Rupp & Jilles Vreeken & Matthias Scheffler, 2020. "Identifying domains of applicability of machine learning models for materials science," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    8. Yunxia Zhang & Yucheng Liu & Zhuo Xu & Haochen Ye & Zhou Yang & Jiaxue You & Ming Liu & Yihui He & Mercouri G. Kanatzidis & Shengzhong (Frank) Liu, 2020. "Nucleation-controlled growth of superior lead-free perovskite Cs3Bi2I9 single-crystals for high-performance X-ray detection," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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