A Fast Approach to Arm Blind Grasping and Placing for Mobile Robot Transportation in Laboratories

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 rare 1-5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches 6-14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements that is required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water 15. The robot operated autonomously over 8 days, performing 688 experiments within a 10-variable experimental space, driven by a batched Bayesian search algorithm 16-18. 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 dexterous 19,20 free-roaming robot 21-24 , automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis. Leverhulme Research Centre for Functional Materials Design, the Engineering and Physical Sciences Research Council (EPSRC) (EP/N004884/1), the Newton Fund (EP/R003580/1), and CSols Ltd. X.W. and Y.B. thank the China Scholarship Council for a Ph.D. studentship. We thank KUKA Robotics for help with gripper design and initial implementation of the robot. Author contributions. B.B. developed the workflow, developed and implemented the robot positioning approach, wrote the control software, designed the bespoke photocatalysis station, and carried out experiments. P.M.M. and V.V.G. developed the optimiser and its interface to the control software. X.L. advised on the photocatalysis workflow. C.M.A., Y.B. and X.L. synthesized materials. Y.B. performed kinetic photocatalysis experiments. X.W. performed NMR analysis and synthesized materials. B.L. carried out initial scavenger screening. R.C. and N.R. helped to build the bespoke stations in the workflow. B.H. analysed the robustness of the system, assisted with the development of control software, and operated the workflow during some experiments. B.A. helped to supervise the automation work. R.S.S. helped to supervise the photocatalysis work. A.I.C. conceived the idea, set up the five hypotheses with BB, and coordinated the research team. Data was interpreted by all authors and the manuscript was prepared by A.I.

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A Fast Approach to Arm Blind Grasping and Placing for Mobile Robot Transportation in Laboratories

Cited by 10 publications

References 10 publications

Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

A mobile robotic chemist

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