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A mobile robotic chemist

Author

Listed:
  • Benjamin Burger

    (University of Liverpool)

  • Phillip M. Maffettone

    (University of Liverpool)

  • Vladimir V. Gusev

    (University of Liverpool)

  • Catherine M. Aitchison

    (University of Liverpool)

  • Yang Bai

    (University of Liverpool)

  • Xiaoyan Wang

    (University of Liverpool)

  • Xiaobo Li

    (University of Liverpool)

  • Ben M. Alston

    (University of Liverpool)

  • Buyi Li

    (University of Liverpool)

  • Rob Clowes

    (University of Liverpool)

  • Nicola Rankin

    (University of Liverpool)

  • Brandon Harris

    (University of Liverpool)

  • Reiner Sebastian Sprick

    (University of Liverpool)

  • Andrew I. Cooper

    (University of Liverpool)

Abstract

Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1–5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6–14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water15. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm16–18. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous19,20 free-roaming robot21–24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.

Suggested Citation

  • Benjamin Burger & Phillip M. Maffettone & Vladimir V. Gusev & Catherine M. Aitchison & Yang Bai & Xiaoyan Wang & Xiaobo Li & Ben M. Alston & Buyi Li & Rob Clowes & Nicola Rankin & Brandon Harris & Rei, 2020. "A mobile robotic chemist," Nature, Nature, vol. 583(7815), pages 237-241, July.
  • Handle: RePEc:nat:nature:v:583:y:2020:i:7815:d:10.1038_s41586-020-2442-2
    DOI: 10.1038/s41586-020-2442-2
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    Citations

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    Cited by:

    1. Xi Zhang & Te Zhang & Xin Wei & Zhanpeng Xiao & Weiwen Zhang, 2024. "Reducing potential dual-use risks in synthetic biology laboratory research: a dynamic model of analysis," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    2. Susan Erikson, 2021. "COVID‐Apps: Misdirecting Public Health Attention in a Pandemic," Global Policy, London School of Economics and Political Science, vol. 12(S6), pages 97-100, July.
    3. 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.
    4. Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Hongyuan Sheng & Jingwen Sun & Oliver Rodríguez & Benjamin B. Hoar & Weitong Zhang & Danlei Xiang & Tianhua Tang & Avijit Hazra & Daniel S. Min & Abigail G. Doyle & Matthew S. Sigman & Cyrille Costent, 2024. "Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    6. Hyuk Jun Yoo & Kwan-Young Lee & Donghun Kim & Sang Soo Han, 2024. "OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Benjamin P. MacLeod & Fraser G. L. Parlane & Connor C. Rupnow & Kevan E. Dettelbach & Michael S. Elliott & Thomas D. Morrissey & Ted H. Haley & Oleksii Proskurin & Michael B. Rooney & Nina Taherimakhs, 2022. "A self-driving laboratory advances the Pareto front for material properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Yifan Xie & Shuo Feng & Linxiao Deng & Aoran Cai & Liyu Gan & Zifan Jiang & Peng Yang & Guilin Ye & Zaiqing Liu & Li Wen & Qing Zhu & Wanjun Zhang & Zhanpeng Zhang & Jiahe Li & Zeyu Feng & Chutian Zha, 2023. "Inverse design of chiral functional films by a robotic AI-guided system," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Ruotong Zhang & Chengzhi Zhang & Xiaoxue Fan & Christina C. K. Au Yeung & Huiyanchen Li & Haisong Lin & Ho Cheung Shum, 2024. "A droplet robotic system enabled by electret-induced polarization on droplet," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    10. Jiyu Cui & Fang Wu & Wen Zhang & Lifeng Yang & Jianbo Hu & Yin Fang & Peng Ye & Qiang Zhang & Xian Suo & Yiming Mo & Xili Cui & Huajun Chen & Huabin Xing, 2023. "Direct prediction of gas adsorption via spatial atom interaction learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    11. Mojan Omidvar & Hangfeng Zhang & Achintha Avin Ihalage & Theo Graves Saunders & Henry Giddens & Michael Forrester & Sajad Haq & Yang Hao, 2024. "Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    12. Zi-Jing Zhang & Shu-Wen Li & João C. A. Oliveira & Yanjun Li & Xinran Chen & Shuo-Qing Zhang & Li-Cheng Xu & Torben Rogge & Xin Hong & Lutz Ackermann, 2023. "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    13. Bin Ouyang & Yan Zeng, 2024. "The rise of high-entropy battery materials," Nature Communications, Nature, vol. 15(1), pages 1-5, December.
    14. Jan Durrer & Prajwal Agrawal & Ali Ozgul & Stephan C. F. Neuhauss & Nitesh Nama & Daniel Ahmed, 2022. "A robot-assisted acoustofluidic end effector," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    15. Wenhao Gao & Priyanka Raghavan & Connor W. Coley, 2022. "Autonomous platforms for data-driven organic synthesis," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    16. Saugat Kandel & Tao Zhou & Anakha V. Babu & Zichao Di & Xinxin Li & Xuedan Ma & Martin Holt & Antonino Miceli & Charudatta Phatak & Mathew J. Cherukara, 2023. "Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    17. Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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