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Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab

Author

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  • Jiagen Li

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Junzi Li

    (Shenzhen University)

  • Rulin Liu

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Yuxiao Tu

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Yiwen Li

    (Hubei University)

  • Jiaji Cheng

    (Hubei University)

  • Tingchao He

    (Shenzhen University)

  • Xi Zhu

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

Abstract

We constructed an intelligent cloud lab that integrates lab automation with cloud servers and artificial intelligence (AI) to detect chirality in perovskites. Driven by the materials acceleration operating system in cloud (MAOSIC) platform, on-demand experimental design by remote users was enabled in this cloud lab. By employing artificial intelligence of things (AIoT) technology, synthesis, characterization, and parameter optimization can be autonomously achieved. Through the remote collaboration of researchers, optically active inorganic perovskite nanocrystals (IPNCs) were first synthesized with temperature-dependent circular dichroism (CD) and inversion control. The inter-structure (structural patterns) and intra-structure (screw dislocations) dual-pattern-induced mechanisms detected by MAOSIC were comprehensively investigated, and offline theoretical analysis revealed the thermodynamic mechanism inside the materials. This self-driving cloud lab enables efficient and reliable collaborations across the world, reduces the setup costs of in-house facilities, combines offline theoretic analysis, and is practical for accelerating the speed of material discovery.

Suggested Citation

  • Jiagen Li & Junzi Li & Rulin Liu & Yuxiao Tu & Yiwen Li & Jiaji Cheng & Tingchao He & Xi Zhu, 2020. "Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15728-5
    DOI: 10.1038/s41467-020-15728-5
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    Cited by:

    1. Amanda A. Volk & Robert W. Epps & Daniel T. Yonemoto & Benjamin S. Masters & Felix N. Castellano & Kristofer G. Reyes & Milad Abolhasani, 2023. "AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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