Abstract
Laboratories in chemistry, biochemistry, and materials science are at the leading edge of technology, discovering molecules and materials to unlock capabilities in energy, catalysis, biotechnology, sustainability, electronics, and more. Yet, most modern laboratories resemble factories from generations past, with a large reliance on humans manually performing synthesis and characterization tasks. Robotics and automation can enable scientific experiments to be conducted faster, more safely, more accurately, and with greater reproducibility, allowing scientists to tackle large societal problems in domains such as health and energy on a shorter timescale. We define five levels of laboratory automation, from laboratory assistance to full automation. We also introduce robotics research challenges that arise when increasing levels of automation and when increasing the generality of tasks within the laboratory. Robots are poised to transform science labs into automated factories of discovery that accelerate scientific progress.
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REFERENCES AND NOTES
1
A. T. Plowright, C. Johnstone, J. Kihlberg, J. Pettersson, G. Robb, R. A. Thompson, Hypothesis driven drug design: Improving quality and effectiveness of the design-make-test-analyse cycle. Drug Discov. Today 17, 56–62 (2012).
2
B. A. Koscher, R. B. Canty, M. A. McDonald, K. P. Greenman, C. J. McGill, C. L. Bilodeau, W. Jin, H. Wu, F. H. Vermeire, B. Jin, T. Hart, T. Kulesza, S.-C. Li, T. S. Jaakkola, R. Barzilay, R. Gómez-Bombarelli, W. H. Green, K. F. Jensen, Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science 382, eadi1407 (2023).
3
K. Sanderson, Automation: Chemistry shoots for the Moon. Nature 568, 577–579 (2019).
4
I. Holland, J. A. Davies, Automation in the life science research laboratory. Front. Bioeng. Biotechnol. 8, 571777 (2020).
5
Y. Jiang, H. Fakhruldeen, G. Pizzuto, L. Longley, A. He, T. Dai, R. Clowes, N. Rankin, A. I. Cooper, Autonomous biomimetic solid dispensing using a dual-arm robotic manipulator. Digit. Discov. 2, 1733–1744 (2023).
6
S. Asche, G. J. T. Cooper, G. Keenan, C. Mathis, L. Cronin, A robotic prebiotic chemist probes long term reactions of complexifying mixtures. Nat. Commun. 12, 3547 (2021).
7
V. Dragone, V. Sans, A. B. Henson, J. M. Granda, L. Cronin, An autonomous organic reaction search engine for chemical reactivity. Nat. Commun. 8, 15733 (2017).
8
J. M. Granda, L. Donina, V. Dragone, D.-L. Long, L. Cronin, Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377–381 (2018).
9
M. Christensen, L. P. E. Yunker, F. Adedeji, F. Häse, L. M. Roch, T. Gensch, G. dos Passos Gomes, T. Zepel, M. S. Sigman, A. Aspuru-Guzik, J. E. Hein, Data-science driven autonomous process optimization. Commun. Chem. 4, 112 (2021).
10
A. Milo, Democratizing synthesis by automation. Science 363, 122–123 (2019).
11
A.-C. Bédard, A. Adamo, K. C. Aroh, M. G. Russell, A. A. Bedermann, J. Torosian, B. Yue, K. F. Jensen, T. F. Jamison, Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).
12
J. Li, S. G. Ballmer, E. P. Gillis, S. Fujii, M. J. Schmidt, A. M. E. Palazzolo, J. W. Lehmann, G. F. Morehouse, M. D. Burke, Synthesis of many different types of organic small molecules using one automated process. Science 347, 1221–1226 (2015).
13
N. Hartrampf, A. Saebi, M. Poskus, Z. P. Gates, A. J. Callahan, A. E. Cowfer, S. Hanna, S. Antilla, C. K. Schissel, A. J. Quartararo, X. Ye, A. J. Mijalis, M. D. Simon, A. Loas, S. Liu, C. Jessen, T. E. Nielsen, B. L. Pentelute, Synthesis of proteins by automated flow chemistry. Science 368, 980–987 (2020).
14
S. Steiner, J. Wolf, S. Glatzel, A. Andreou, J. M. Granda, G. Keenan, T. Hinkley, G. Aragon-Camarasa, P. J. Kitson, D. Angelone, L. Cronin, Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).
15
C. W. Coley, D. A. Thomas, J. A. M. Lummiss, J. N. Jaworski, C. P. Breen, V. Schultz, T. Hart, J. S. Fishman, L. Rogers, H. Gao, R. W. Hicklin, P. P. Plehiers, J. Byington, J. S. Piotti, W. H. Green, A. J. Hart, T. F. Jamison, K. F. Jensen, A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).
16
A. Sparkes, W. Aubrey, E. Byrne, A. Clare, M. N. Khan, M. Liakata, M. Markham, J. Rowland, L. N. Soldatova, K. E. Whelan, M. Young, R. D. King, Towards robot scientists for autonomous scientific discovery. Autom. Exp. 2, 1 (2010).
17
H. J. Yoo, N. Kim, H. Lee, D. Kim, L. T. C. Ow, H. Nam, C. Kim, S. Y. Lee, K.-Y. Lee, D. Kim, S. S. Han, Bespoke metal nanoparticle synthesis at room temperature and discovery of chemical knowledge on nanoparticle growth via autonomous experimentations. Adv. Funct. Mater. 34, 2312561 (2024).
18
F. Kong, L. Yuan, Y. F. Zheng, W. Chen, Automatic liquid handling for life science: A critical review of the current state of the art. J. Lab. Autom. 17, 169–185 (2012).
19
M. H. Reis, F. A. Leibfarth, L. M. Pitet, Polymerizations in continuous flow: Recent advances in the synthesis of diverse polymeric materials. ACS Macro Lett. 9, 123–133 (2020).
20
A. Slattery, Z. Wen, P. Tenblad, J. Sanjosé-Orduna, D. Pintossi, T. den Hartog, T. Noël, Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 383, eadj1817 (2024).
21
A. A. Volk, R. W. Epps, D. T. Yonemoto, B. S. Masters, F. N. Castellano, K. G. Reyes, M. Abolhasani, AlphaFlow: Autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nat. Commun. 14, 1403 (2023).
22
M. Walker, G. Pizzuto, H. Fakhruldeen, A. I. Cooper, Go with the flow: Deep learning methods for autonomous viscosity estimations. Digit. Discov. 2, 1540–1547 (2023).
23
S. Kleine-Wechelmann, K. Bastiaanse, M. Freundel, C. Becker-Asano, Designing the mobile robot Kevin for a life science laboratory, in 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (IEEE, 2022), pp. 870–875.
24
A. Angelopoulos, M. Verber, C. McKinney, J. Cahoon, R. Alterovitz, High-accuracy injection using a mobile manipulation robot for chemistry lab automation, in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2023), pp. 10102–10109.
25
B. Burger, P. M. Maffettone, V. V. Gusev, C. M. Aitchison, Y. Bai, X. Wang, X. Li, B. M. Alston, B. Li, R. Clowes, N. Rankin, B. Harris, R. S. Sprick, A. I. Cooper, A mobile robotic chemist. Nature 583, 237–241 (2020).
26
Q. Zhu, F. Zhang, Y. Huang, H. Xiao, L. Zhao, X. Zhang, T. Song, X. Tang, X. Li, G. He, B. Chong, J. Zhou, Y. Zhang, B. Zhang, J. Cao, M. Luo, S. Wang, G. Ye, W. Zhang, X. Chen, S. Cong, D. Zhou, H. Li, J. Li, G. Zou, W. Shang, J. Jiang, Y. Luo, An all-round AI-Chemist with scientific mind. Natl. Sci. Rev. 9, nwac190 (2022).
27
H. Liu, N. Stoll, S. Junginger, K. Thurow, Mobile robotic transportation in laboratory automation: Multi-robot control, robot-door integration and robot-human interaction, in 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014) (IEEE, 2014), pp. 1033–1038.
28
P. Dettinger, T. Kull, G. Arekatla, N. Ahmed, Y. Zhang, F. Schneiter, A. Wehling, D. Schirmacher, S. Kawamura, D. Loeffler, T. Schroeder, Open-source personal pipetting robots with live-cell incubation and microscopy compatibility. Nat. Commun. 13, 2999 (2022).
29
N. J. Szymanski, B. Rendy, Y. Fei, R. E. Kumar, T. He, D. Milsted, M. J. McDermott, M. Gallant, E. D. Cubuk, A. Merchant, H. Kim, A. Jain, C. J. Bartel, K. Persson, Y. Zeng, G. Ceder, An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).
30
T. Zonta, C. A. da Costa, R. da Rosa Righi, M. J. de Lima, E. S. da Trindade, G. P. Li, Predictive maintenance in the Industry 4.0: A systematic literature review. Comput. Ind. Eng. 150, 106889 (2020).
31
A. M. Lunt, H. Fakhruldeen, G. Pizzuto, L. Longley, A. White, N. Rankin, R. Clowes, B. Alston, L. Gigli, G. M. Day, A. I. Cooper, S. Y. Chong, Modular, multi-robot integration of laboratories: An autonomous workflow for solid-state chemistry. Chem. Sci. 15, 2456–2463 (2024).
32
H. Fakhruldeen, G. Pizzuto, J. Glowacki, A. I. Cooper, ARChemist: Autonomous robotic chemistry system architecture, in 2022 International Conference on Robotics and Automation (ICRA) (IEEE, 2022), pp. 6013–6019.
33
Emerald Cloud Lab: Remote Controlled Life Sciences Lab; www.emeraldcloudlab.com/.
34
N. Yoshikawa, M. Skreta, K. Darvish, S. Arellano-Rubach, Z. Ji, L. Bjørn Kristensen, A. Z. Li, Y. Zhao, H. Xu, A. Kuramshin, A. Aspuru-Guzik, F. Shkurti, A. Garg, Large language models for chemistry robotics. Auton. Robots 47, 1057–1086 (2023).
35
J. H. Montoya, M. Aykol, A. Anapolsky, C. B. Gopal, P. K. Herring, J. S. Hummelshøj, L. Hung, H.-K. Kwon, D. Schweigert, S. Sun, S. K. Suram, S. B. Torrisi, A. Trewartha, B. D. Storey, Toward autonomous materials research: Recent progress and future challenges. Appl. Phys. Rev. 9, 011405 (2022).
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