Self-driving laboratory for accelerated discovery of thin-film materials

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Science Advances  13 May 2020:
Vol. 6, no. 20, eaaz8867
DOI: 10.1126/sciadv.aaz8867
  • Fig. 1 The Ada self-driving laboratory.

    (A) The self-driving laboratory is based on a modular robotic platform that interacts with objects using a rotatable pneumatic gripper on a polar robotic arm achieving 10-μm repeatability and a maximum velocity of ~1 m/s. (B) Fluid handling is achieved using disposable pipette tips that can be press-fit onto and removed from the arm’s pipette mount by the robot. Pipetting with a mean accuracy of 5 μl is achieved using a syringe pump connected to the pipette mount. (C) Substrate handling is achieved using a vacuum substrate handler gripped by the robotic arm. (D) Configuration of the robotic platform for a specific experimental workflow is achieved by mounting an appropriate collection of experimental modules on the robot; here, the Ada platform is shown equipped for the synthesis and characterization of thin-film materials.

  • Fig. 2 Ada uses an autonomous optimization workflow.

    The autonomous workflow involves iterative experimentation with the goal of discovering a thin-film composition with the highest possible pseudomobility. Each iteration of the workflow involves the following: (1) mixing an hole transport material (HTM)-dopant–additive ink, (2) spin coating the ink onto a substrate, (3) thermally annealing for a variable amount of time, (4) imaging with a visible-light camera, (5) acquiring ultraviolet–visible–near-infrared (UV-vis-NIR) spectra in reflection and transmission modes, (6) measuring the current-voltage (I-V) curve of the film with a four-point probe, (7) computing a pseudomobility based on the I-V and spectroscopic data, and (8) feeding this pseudomobility into the ChemOS (14) orchestration software and the Phoenics Bayesian optimization algorithm (15), which then designs the next experiment.

  • Fig. 3 Results of thin-film pseudomobility optimization carried out by the self-driving laboratory.

    (A) Experimental values for cobalt doping ratio, annealing time, and maximum measured pseudomobility as a function of the number of experiments performed for two independent optimization runs. (B) The pseudomobility response surface and sampled points for the second (blue; left) optimization run. The algorithm initially found a local maximum and then found the global maximum of the sampled parameter space.

Supplementary Materials

  • Supplementary Materials

    Self-driving laboratory for accelerated discovery of thin-film materials

    B. P. MacLeod, F. G. L. Parlane, T. D. Morrissey, F. Häse, L. M. Roch, K. E. Dettelbach, R. Moreira, L. P. E. Yunker, M. B. Rooney, J. R. Deeth, V. Lai, G. J. Ng, H. Situ, R. H. Zhang, M. S. Elliott, T. H. Haley, D. J. Dvorak, A. Aspuru-Guzik, J. E. Hein, C. P. Berlinguette

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