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A mobile robotic chemist

A mobile robotic chemist


Robotics

A mobile robotic chemist

AbstractTechnologies 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 rare1,2,3,4,5. Likewise, experimental complexity scales exponentially with the number of variables, restricting…

A mobile robotic chemist

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 rare1,2,3,4,5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6,7,8,9,10,11,12,13,14 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 algorithm16,17,18. 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 robot21,22,23,24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.

Data availability

The implementation of the liquid-dispensing station, photolysis station and the workflow, along with three-dimensional designs for labware developed in the project, are available at https://bitbucket.org/ben_burger/kuka_workflow, the code for the robot at and the Bayesian optimizer is available at https://github.com/Taurnist/kuka_workflow_tantalus and https://github.com/CooperComputationalCaucus/kuka_optimizer. Additional design details can be obtained from the authors upon request.

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Acknowledgements

We acknowledge financial support from the Leverhulme Trust via the Leverhulme Research Centre for Functional Materials Design, the Engineering and Physical Sciences Research Council (EPSRC) (grant number EP/N004884/1), the Newton Fund (grant number EP/R003580/1), and CSols Ltd. X.W. and Y.B. thank the China Scholarship Council for a PhD studentship. We thank KUKA Robotics for help with gripper design and the initial implementation of the robot.

Author information

Affiliations

  1. Leverhulme Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK

    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, Reiner Sebastian Sprick & Andrew I. Cooper

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 optimizer 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.M.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 B.B., and coordinated the research team. Data was interpreted by all authors and the manuscript was prepared by A.I.C., B.B., P.M.M., V.V.G. and R.S.S.

Corresponding author

Correspondence to
Andrew I. Cooper.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Volker Krueger, Tyler McQuade and Magda Titirici for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Mobile robotic chemist.

The mobile robot used for this project, shown here performing a six-point calibration with respect to the black location cube that is attached to the bench, in this case associated with the solid cartridge station (see also Supplementary Fig. 11 and Extended Data Fig. 3a).

Extended Data Fig. 2 Laboratory space used for the autonomous experiments.

The key locations in the workflow are labelled. Other than the black location cubes that are fixed to the benches to allow positioning (see also Extended Data Fig. 1), the laboratory is otherwise unmodified.

Extended Data Fig. 3 Stations in the workflow.

a, Photograph showing the robot at the solid dispensing / cartridge station. The two cartridge hotels can hold up to 20 different solids; here, four cartridges are located in the hotel on the left. The door of the Quantos dispenser is opened using custom workflow software that interfaces with the command software that is supplied with the instrument before loading the correct solid dispensing cartridge into the instrument (Supplementary Video 3). Since the KUKA Mobile Robot is free-roaming and has an 820 mm reach, it would be simple to extend this modular approach to hundreds or even thousands of different solids given sufficient laboratory space. b, Photograph showing the KUKA Mobile Robot at the photolysis station (see also Supplementary Videos 3, 6). c, Photograph showing the KUKA Mobile Robot at the combined liquid handling/capping station. The robot can reach both the liquid stations and the Liverpool Inertization Capper-Crimper (LICC) station after six-point positioning, such that liquid addition, headspace inertization and capping can be carried out in a single coordinated process (see Supplementary Videos 3, 5), without any position recalibration. d, Photograph of the KUKA Mobile Robot parked at the headspace gas chromatography (GC) station. The gas chromatography instrument is a standard commercial instrument and was unmodified in this workflow.

Extended Data Fig. 4 Hydrogen evolution rates for candidate bioderived sacrificial hole scavengers.

Results of a robotic screen for sacrificial hole scavengers using the mobile robot workflow. Of the 30 bioderived molecules trialed, only cysteine was found to compete with the petrochemical amine, triethanolamine. Scavengers are labelled with the concentration of the stock solution that was used (5 ml volume; 5 mg P10). The error bars show the standard deviation.

Extended Data Fig. 5 Multipurpose gripper used in the workflow.

The gripper is shown grasping various objects. a, The empty gripper; b, gripper holding a capped sample vial (top grasp); c, gripper holding an uncapped sample vial (side grasp); d, gripper holding a solid-dispensing cartridge; and e, gripper holding a full sample rack using an outwards grasp that locks into recesses in the rack. The same gripper was also used to activate the gas chromatography instrument using a physical button press (see Supplementary Video 3; 1 min 52 s).

Extended Data Fig. 6 Timescales for steps in the workflow.

Average timescales for the various steps in the workflow (sample preparation, photolysis and analysis) for a batch of 16 experiments. These averages were calculated over 46 separate batches. These average times include the time taken for the loading and unloading steps (for example, the photolysis time itself was 60 min; loading and unloading takes an average of 28 min per batch). The slowest step in the workflow is the gas chromatography analysis.

Supplementary information

Supplementary Information

This file contains Supplementary Methods and Supplementary Notes. This file presents the technical specifications of the robot, the experimental stations, workflow benchmarking, the sacrificial hole scavenger screen, control experiments, in silico benchmarking of the search algorithm, experimental robustness tests, and 24/7 monitoring of the workflow.

Supplementary Data

This file contains the data that was obtained during the autonomous search. This includes the masses and volumes suggested by the optimizer, mass and volumes measured during the autonomous experiment, and the GC measurements (amounts of hydrogen evolved).

Supplementary Video 1

This video shows the autonomous system from a bird’s eye view running over 48 hours with a speed up factor of 2,880.

Supplementary Video 2

This video shows the autonomous system from a bird’s eye view running in the dark; speed up factor = 360.

Supplementary Video 3

This video shows a close-up of all steps in the workflow at various speeds (20x – 100x).

Supplementary Video 4

This video shows a liquid module dispensing 1 mL of water using PID control (double speed).

Supplementary Video 5

This video shows the cap crimping process (double speed).

Supplementary Video 6

This video shows the vibratory mixing used in the photolysis station (double speed).

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Cite this article

Burger, B., Maffettone, P.M., Gusev, V.V. et al. A mobile robotic chemist.
Nature 583, 237–241 (2020). https://doi.org/10.1038/s41586-020-2442-2

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Robotics

A mobile robotic chemist

AbstractTechnologies 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 rare1,2,3,4,5. Likewise, experimental complexity scales exponentially with the number of variables, restricting…

A mobile robotic chemist

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 rare1,2,3,4,5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6,7,8,9,10,11,12,13,14 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 algorithm16,17,18. 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 robot21,22,23,24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.

Data availability

The implementation of the liquid-dispensing station, photolysis station and the workflow, along with three-dimensional designs for labware developed in the project, are available at https://bitbucket.org/ben_burger/kuka_workflow, the code for the robot at and the Bayesian optimizer is available at https://github.com/Taurnist/kuka_workflow_tantalus and https://github.com/CooperComputationalCaucus/kuka_optimizer. Additional design details can be obtained from the authors upon request.

References

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    ADS 
    CAS 
    Article 

    Google Scholar
     

  2. 2.

    Woodley, S. M. & Catlow, R. Crystal structure prediction from first principles. Nat. Mater. 7, 937–946 (2008).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  3. 3.

    Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).

    ADS 
    Article 

    Google Scholar
     

  4. 4.

    Collins, C. et al. Accelerated discovery of two crystal structure types in a complex inorganic phase field. Nature 546, 280–284 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  5. 5.

    Davies, D. W. et al. Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure. Chem. Sci. 9, 1022–1030 (2018).

    CAS 
    Article 

    Google Scholar
     

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    King, R. D. Rise of the robo scientists. Sci. Am. 304, 72–77 (2011).

    ADS 
    Article 

    Google Scholar
     

  7. 7.

    Li, J. et al. Synthesis of many different types of organic small molecules using one automated process. Science 347, 1221–1226 (2015).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  8. 8.

    Dragone, V., Sans, V., Henson, A. B., Granda, J. M. & Cronin, L. An autonomous organic reaction search engine for chemical reactivity. Nat. Commun. 8, 15733 (2017).

    ADS 
    Article 

    Google Scholar
     

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    Bédard, A.-C. et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).

    ADS 
    Article 

    Google Scholar
     

  10. 10.

    Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

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    Tabor, D. P. et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

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    Langner, S. et al. Beyond ternary OPV: high-throughput experimentation and self-driving laboratories optimize multi-component systems. Preprint at https://arxiv.org/abs/1909.03511 (2019).

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    MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Preprint at https://arxiv.org/abs/1906.05398 (2019).

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    Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).

    CAS 
    Article 

    Google Scholar
     

  15. 15.

    Wang, Z., Li, C. & Domen, K. Recent developments in heterogeneous photocatalysts for solar-driven overall water splitting. Chem. Soc. Rev. 48, 2109–2125 (2019).

    CAS 
    Article 

    Google Scholar
     

  16. 16.

    Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & Freitas, N. D. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016).

    Article 

    Google Scholar
     

  17. 17.

    Häse, F., Roch, L. M., Kreisbeck, C. & Aspuru-Guzik, A. Phoenics: a Bayesian optimizer for chemistry. ACS Cent. Sci. 4, 1134–1145 (2018).

    Article 

    Google Scholar
     

  18. 18.

    Roch, L. M. et al. ChemOS: orchestrating autonomous experimentation. Sci. Robot. 3, eaat5559 (2018).

    Article 

    Google Scholar
     

  19. 19.

    Chen, C.-L., Chen, T.-R., Chiu, S.-H. & Urban, P. L. Dual robotic arm “production line” mass spectrometry assay guided by multiple Arduino-type microcontrollers. Sens. Actuat. B 239, 608–616 (2017).

    CAS 
    Article 

    Google Scholar
     

  20. 20.

    Fleischer, H. et al. Analytical measurements and efficient process generation using a dual-arm robot equipped with electronic pipettes. Energies 11, 2567 (2018).

    Article 

    Google Scholar
     

  21. 21.

    Liu, H., Stoll, N., Junginger, S. & Thurow, K. Mobile robot for life science automation. Int. J. Adv. Robot. Syst. 10, 288 (2013).

    Article 

    Google Scholar
     

  22. 22.

    Liu, H., Stoll, N., Junginger, S. & Thurow, K. A fast approach to arm blind grasping and placing for mobile robot transportation in laboratories. Int. J. Adv. Robot. Syst. 11, 43 (2014).

    Article 

    Google Scholar
     

  23. 23.

    Abdulla, A. A., Liu, H., Stoll, N. & Thurow, K. A new robust method for mobile robot multifloor navigation in distributed life science laboratories. J. Contrib. Sci. Eng. 2016, 3589395 (2016).

    MATH 

    Google Scholar
     

  24. 24.

    Dömel, A. et al. Toward fully autonomous mobile manipulation for industrial environments. Int. J. Adv. Robot. Syst. 14, https://doi.org/10.1177/1729881417718588 (2017).

  25. 25.

    Schweidtmann, A. M. et al. Machine learning meets continuous flow chemistry: automated optimization towards the Pareto front of multiple objectives. Chem. Eng. J. 352, 277–282 (2018).

    CAS 
    Article 

    Google Scholar
     

  26. 26.

    Zhi, L. et al. Robot-accelerated perovskite investigation and discovery (RAPID): 1. Inverse temperature crystallization. Preprint at https://doi.org/10.26434/chemrxiv.10013090.v1 (2019).

  27. 27.

    Matsuoka, S. et al. Photocatalysis of oligo (p-phenylenes): photoreductive production of hydrogen and ethanol in aqueous triethylamine. J. Phys. Chem. 95, 5802–5808 (1991).

    CAS 
    Article 

    Google Scholar
     

  28. 28.

    Shu, G., Li, Y., Wang, Z., Jiang, J.-X. & Wang, F. Poly(dibenzothiophene-S,S-dioxide) with visible light-induced hydrogen evolution rate up to 44.2 mmol h−1 g−1 promoted by K2HPO4. Appl. Catal. B 261, 118230 (2020).

    CAS 
    Article 

    Google Scholar
     

  29. 29.

    Pellegrin, Y. & Odobel, F. Sacrificial electron donor reagents for solar fuel production. C. R. Chim. 20, 283–295 (2017).

    CAS 
    Article 

    Google Scholar
     

  30. 30.

    Sakimoto, K. K., Zhang, S. J. & Yang, P. Cysteine–cystine photoregeneration for oxygenic photosynthesis of acetic acid from CO2 by a tandem inorganic–biological hybrid system. Nano Lett. 16, 5883–5887 (2016).

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    CAS 
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Acknowledgements

We acknowledge financial support from the Leverhulme Trust via the Leverhulme Research Centre for Functional Materials Design, the Engineering and Physical Sciences Research Council (EPSRC) (grant number EP/N004884/1), the Newton Fund (grant number EP/R003580/1), and CSols Ltd. X.W. and Y.B. thank the China Scholarship Council for a PhD studentship. We thank KUKA Robotics for help with gripper design and the initial implementation of the robot.

Author information

Affiliations

  1. Leverhulme Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK

    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, Reiner Sebastian Sprick & Andrew I. Cooper

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 optimizer 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.M.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 B.B., and coordinated the research team. Data was interpreted by all authors and the manuscript was prepared by A.I.C., B.B., P.M.M., V.V.G. and R.S.S.

Corresponding author

Correspondence to
Andrew I. Cooper.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Volker Krueger, Tyler McQuade and Magda Titirici for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Mobile robotic chemist.

The mobile robot used for this project, shown here performing a six-point calibration with respect to the black location cube that is attached to the bench, in this case associated with the solid cartridge station (see also Supplementary Fig. 11 and Extended Data Fig. 3a).

Extended Data Fig. 2 Laboratory space used for the autonomous experiments.

The key locations in the workflow are labelled. Other than the black location cubes that are fixed to the benches to allow positioning (see also Extended Data Fig. 1), the laboratory is otherwise unmodified.

Extended Data Fig. 3 Stations in the workflow.

a, Photograph showing the robot at the solid dispensing / cartridge station. The two cartridge hotels can hold up to 20 different solids; here, four cartridges are located in the hotel on the left. The door of the Quantos dispenser is opened using custom workflow software that interfaces with the command software that is supplied with the instrument before loading the correct solid dispensing cartridge into the instrument (Supplementary Video 3). Since the KUKA Mobile Robot is free-roaming and has an 820 mm reach, it would be simple to extend this modular approach to hundreds or even thousands of different solids given sufficient laboratory space. b, Photograph showing the KUKA Mobile Robot at the photolysis station (see also Supplementary Videos 3, 6). c, Photograph showing the KUKA Mobile Robot at the combined liquid handling/capping station. The robot can reach both the liquid stations and the Liverpool Inertization Capper-Crimper (LICC) station after six-point positioning, such that liquid addition, headspace inertization and capping can be carried out in a single coordinated process (see Supplementary Videos 3, 5), without any position recalibration. d, Photograph of the KUKA Mobile Robot parked at the headspace gas chromatography (GC) station. The gas chromatography instrument is a standard commercial instrument and was unmodified in this workflow.

Extended Data Fig. 4 Hydrogen evolution rates for candidate bioderived sacrificial hole scavengers.

Results of a robotic screen for sacrificial hole scavengers using the mobile robot workflow. Of the 30 bioderived molecules trialed, only cysteine was found to compete with the petrochemical amine, triethanolamine. Scavengers are labelled with the concentration of the stock solution that was used (5 ml volume; 5 mg P10). The error bars show the standard deviation.

Extended Data Fig. 5 Multipurpose gripper used in the workflow.

The gripper is shown grasping various objects. a, The empty gripper; b, gripper holding a capped sample vial (top grasp); c, gripper holding an uncapped sample vial (side grasp); d, gripper holding a solid-dispensing cartridge; and e, gripper holding a full sample rack using an outwards grasp that locks into recesses in the rack. The same gripper was also used to activate the gas chromatography instrument using a physical button press (see Supplementary Video 3; 1 min 52 s).

Extended Data Fig. 6 Timescales for steps in the workflow.

Average timescales for the various steps in the workflow (sample preparation, photolysis and analysis) for a batch of 16 experiments. These averages were calculated over 46 separate batches. These average times include the time taken for the loading and unloading steps (for example, the photolysis time itself was 60 min; loading and unloading takes an average of 28 min per batch). The slowest step in the workflow is the gas chromatography analysis.

Supplementary information

Supplementary Information

This file contains Supplementary Methods and Supplementary Notes. This file presents the technical specifications of the robot, the experimental stations, workflow benchmarking, the sacrificial hole scavenger screen, control experiments, in silico benchmarking of the search algorithm, experimental robustness tests, and 24/7 monitoring of the workflow.

Supplementary Data

This file contains the data that was obtained during the autonomous search. This includes the masses and volumes suggested by the optimizer, mass and volumes measured during the autonomous experiment, and the GC measurements (amounts of hydrogen evolved).

Supplementary Video 1

This video shows the autonomous system from a bird’s eye view running over 48 hours with a speed up factor of 2,880.

Supplementary Video 2

This video shows the autonomous system from a bird’s eye view running in the dark; speed up factor = 360.

Supplementary Video 3

This video shows a close-up of all steps in the workflow at various speeds (20x – 100x).

Supplementary Video 4

This video shows a liquid module dispensing 1 mL of water using PID control (double speed).

Supplementary Video 5

This video shows the cap crimping process (double speed).

Supplementary Video 6

This video shows the vibratory mixing used in the photolysis station (double speed).

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Burger, B., Maffettone, P.M., Gusev, V.V. et al. A mobile robotic chemist.
Nature 583, 237–241 (2020). https://doi.org/10.1038/s41586-020-2442-2

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