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Autonomous experiments using active learning and AI

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Active learning and automation will not easily liberate humans from laboratory workflows. Before they can really impact materials research, artificial intelligence systems will need to be carefully set up to ensure their robust operation and their ability to deal with both epistemic and stochastic errors. As autonomous experiments become more widely available, it is essential to think about how to embed reproducibility, reconfigurability and interoperability in the design of autonomous labs.

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Fig. 1: Future outlook: autonomous labs connected in an AI network.

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Acknowledgements

The authors thank Y. Tian for insightful discussions and R. S. Indradjaja for giving feedback on the manuscript. They acknowledge support by DTRA (award no. HDTRA1-20-2-0002) Interaction of Ionizing Radiation with Matter (IIRM) University Research Alliance (URA).

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Authors and Affiliations

  1. Department of Materials Science and Engineering, MIT, Cambridge, MA, USA

    Zhichu Ren, Zhen Zhang & Ju Li

  2. Xinterra, Singapore, Singapore

    Zekun Ren & Tonio Buonassisi

  3. Department of Mechanical Engineering, MIT, Cambridge, MA, USA

    Tonio Buonassisi

  4. Department of Nuclear Science and Engineering, MIT, Cambridge, MA, USA

    Ju Li

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  1. Zhichu Ren
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  2. Zekun Ren
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  3. Zhen Zhang
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  4. Tonio Buonassisi
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  5. Ju Li
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Corresponding authors

Correspondence to Tonio Buonassisi or Ju Li.

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Competing interests

Zekun Ren and T.B. are co-founders of Xinterra Pte. Ltd, a startup focused on applying active learning to accelerate the development of materials for sustainability. The other authors declare no competing interests.

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Ren, Z., Ren, Z., Zhang, Z. et al. Autonomous experiments using active learning and AI. Nat Rev Mater 8, 563–564 (2023). https://doi.org/10.1038/s41578-023-00588-4

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