Project Details

While there are several excellent platforms in chemistry and materials science for low-cost self-driving laboratories or SDLs (i.e., autonomous research laboratories) [1, 2, 3, 4, 5, 6], for wider adoption of a demo, it needs to be cheaper, smaller, and simpler to set up while still preserving the functional aspects of a materials acceleration platform (MAP). In programming, a minimal working example (MWE) “is a code snippet that can be copied and pasted into an empty ... file and still have the same features (working), and that does not include unnecessary details (minimal). [7]” Here, we pose the question:

What does a minimal working example look like for a self-driving laboratory?

We introduce the idea of a self-driving optics demo for less than $100, a square foot of desk space, and an hour of total setup time. See the following video for an introduction of how this works:

We believe our demonstration adequately meets the minimal, complete, and reproducible requirements of a MWE SDL (Table 1)

	Programming	Self-driving Laboratory
Minimal	Use as little code as possible that still produces the same problem [7]	Minimize the cost, size, and setup while still being an SDL
Complete	Provide all parts needed to reproduce the problem in the question itself [7]	Provide software with documentation and a bill of materials with setup instructions
Reproducible	Test the code you’re about to provide to make sure it reproduces the problem [7]	Benchmark the SDL using a fixed configuration and verify the results are expected

as well as the high-level definition of a MAP (though not materials specific):

[A system that] carries out high-throughput and/or automated experiments, the results of which are fed back into the AI that guides the selection of subsequent rounds of experimentation to optimize or make a discovery.

The demonstration involves controlling the brightness of light-emitting diodes (LEDs) at fixed wavelengths, sensing the light mixture via a discrete-channel spectrometer, decision-making to tune the inputs to best match the desired spectrum, and optionally, cloud-based simulations to aid in decision-making. The setup is summarized in Figure 1.

Figure 1: Summary of the self-driving laboratory demonstration (SDL-Demo). A microcontroller (Raspberry Pi (RPi)) sends commands to a dimmable red green blue (RGB) light-emitting diode (LED) to control the brightness at different wavelengths. A spectrophotometer measures the light signal at eight individual wavelengths. The microcontroller reads the intensity values from the spectrophotometer and uses these newly measured values and prior information (including e.g. prior measurements and physics-based simulations performed in the cloud) to choose the next set of LED parameters in an effort to better match a target spectrum. The setup adequately meets the minimal requirement of a minimal working example (MWE) self-driving laboratory (SDL) by costing less than 100 USD, occupying less than 1 ft2 (0.1 m2) of desk space, and requiring less than 1 h of setup time.

For more context, see a recording based on my talk at the 2022 acceleration conference in Toronto.

Build Instructions

References

[1] D. Caramelli, D. Salley, A. Henson, G. A. Camarasa, S. Sharabi, G. Keenan, L. Cronin, Networking chemical robots for reaction multitasking, Nature Communications 9 (2018) 3406. URL: https://www.nature.com/articles/s41467-018-05828-8. doi:10.1038/s41467 -018-05828-8, number: 1 Publisher: Nature Publishing Group.

[2] T. Fuhrmann, D. I. Ahmed, L. Arikson, M. Wirth, M. L. Miller, E. Li, A. Lam, P. Blikstein, I. Riedel-Kruse, Scientific Inquiry in Middle Schools by combining Computational Thinking, Wet Lab Experiments, and Liquid Handling Robots, in: Interaction Design and Children, ACM, Athens Greece, 2021, pp. 444– 449. URL: https://dl.acm.org/doi/10.1145/3459990.3465180. doi:10.1145/3459990.34 65180.

[3] A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation, 2022. URL: http://arxiv.org/abs/2204.04187. doi:10.48550/arX iv.2204.04187, arXiv:2204.04187 [cond-mat].

[4] Tonio Buonassisi [@toniobuonassisi], From Boston to Seattle, MIT students write machinelearning code to control an autonomous robot at SMART, MIT’s research enterprise in Singapore. @LiuZhe mit queues up the next job for our color-matching robot, for our 2.s986 Applied ML class. #opentrons #appliedml #BoB @MITMechE https://t.co/LEnbzen6dc, 2020. URL: https://mobile.twitter.com/toniobuonassisi/status/1328988087376961538.

[5] J. M. P. Gutierrez, T. Hinkley, J. W. Taylor, K. Yanev, L. Cronin, Evolution of oil droplets in a chemorobotic platform, Nature Communications 5 (2014) 5571. URL: http://www.nature.com/articles/ncomms6571. doi:10.1038/ncomms6571

[6] S. Vargas, S. Zamirpour, S. Menon, A. Rothman, F. H¨ase, T. Tamayo-Mendoza, J. Romero, S. Sim, T. Menke, A. Aspuru-Guzik, TeamBased Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions, Journal of Chemical Education 97 (2020) 689–694. URL: https://pubs.acs.org/doi/10.1021/acs.jchemed.9b00603. doi:10.1021/acs.jchemed.9b00603.

[7] Minimal working examples, 2022. URL: https://leanprover-community.github.io/mwe.html.