Am I too hopeful for self-driving labs?

I have always been bullish and captivated by the concept of automated materials design through cutting-edge lab facilities and computation.  When I see all the advances in robotic systems over the past decade I do think we are headed in the direction of material synthesis, characterization, and testing data in a closed-loop system. Maybe we have already started the initial seed. In my view, it does seem that the integration of robotics, data science, and AI is now catalyzing a new era in the field of materials science. I'm hoping that we eventually get the to point that self-driving labs for accelerated materials discovery.

The pressing question is whether we are on the verge of achieving fully automated materials design. Can researchers simply specify a desired material property with constraints and rely on a self-driving lab to devise the synthesis route and characterize properties? Encouragingly, we seem to be heading in that direction. Academic groups like those led by Alán Aspuru-Guzik at the University of Toronto [1] and Taylor Sparks at the University of Utah [2] have laid the groundwork for self-driving labs, employing robotics for high-throughput experimentation, data science for handling vast volumes of data, and AI for enhanced prediction and optimization. These efforts are impressive, but further advancements will be required to enable more diverse access to synthesis routes, types of characterization, and testing. The goal should eventually be to develop labs capable of employing various methods for creating and testing materials to meet multi-objective property targets. The success of self-driving labs will probably occur through the collaboration between academia and industry, which will help with overcoming the challenges posed by high capital costs and ensuring the widespread adoption of self-driving labs.

My opinion is that the rapid development and growing capabilities of AI systems will continue to be a driving force behind self-driving labs. These narrow/specialized AI systems, although maybe not yet exhibiting general intelligence, are becoming increasingly adept at processing large datasets and extracting valuable insights using techniques like Bayesian optimization [3]. This enables researchers to explore vast design spaces, generate new hypotheses, and iteratively refine their experiments to identify optimal materials design criteria. I'll posit that the convergence of robotics, data science, and AI will revolutionize the field of materials science given researchers don't overpromise and several strong case studies are realized. This will pave the way for new and groundbreaking technologies that can only be realized if materials can be discovered and regularly synthesized given design criteria. As someone who has been studying Bayesian techniques for the past three years and has long been interested in self-driving labs, I hope I get the chance to work on this.

References

[1] 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, Self-driving laboratory for accelerated discovery of thin-film materials, Sci. Adv. 6 (2020) eaaz8867. https://doi.org/10.1126/sciadv.aaz8867.

[2] S.G. Baird, T.D. Sparks, What is a minimal working example for a self-driving laboratory?, Matter. 5 (2022) 4170–4178. https://doi.org/10.1016/j.matt.2022.11.007.

[3] F. Häse, L. M. Roch, A. Aspuru-Guzik, Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories, Chemical Science. 9 (2018) 7642–7655. https://doi.org/10.1039/C8SC02239A.

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