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Reproducibility in automated chemistry laboratories using computer science abstractions

Nature Synthesis volume 3, pages 1327–1339 (2024)Cite this article

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Abstract

While abstraction is critical for the transferability of automated laboratory science in (bio)chemical and materials sciences, its improper implementation is a technical debt taken against the reproducibility of experimental results. Over the decades, computer science has developed guidelines and strategies for how abstractions are captured in programming languages—particularly concerning the substitutability of implementations of abstracted ideas and the clear definition of the contexts in which abstractions are used. However, few programming languages developed for automated experiments fully leverage the wisdom learned in computer science. To achieve collaborative sharing of scientific knowledge via automated laboratories, the way that experimental protocols are codified and interpreted by machine agents must use abstractions responsibly and with reproducibility, rather than solely transferability, at its core. This Review discusses how computer science principles of abstraction can be translated to create more reproducible automation as an enabler for the acceleration of collaborative research with self-driving laboratories.

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Fig. 1: Comparisons of the repeatability and reproducibility of experimental results.
Fig. 2: Various implementations of the same abstraction leading to different results.
Fig. 3: Disparity between abstractly equivalent but hardware-dependent implementations for a Java sorting algorithm.
Fig. 4: Visualizations of the approaches described.
Fig. 5: Example excerpts of different languages for prescribing automated experiments.

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Acknowledgements

We would like to thank H. Moran (North Carolina State University) for discussions around making the computer science jargon more accessible. M.A. gratefully acknowledges financial support from the University of North Carolina Research Opportunities Initiative (UNC-ROI) and National Science Foundation (award nos. 1940959 and 2208406).

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  1. Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA

    Richard B. Canty & Milad Abolhasani

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R.B.C. conceived and draughted the work. M.A. and R.B.C. reviewed and edited the manuscript.

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Correspondence to Milad Abolhasani.

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Canty, R.B., Abolhasani, M. Reproducibility in automated chemistry laboratories using computer science abstractions. Nat. Synth 3, 1327–1339 (2024). https://doi.org/10.1038/s44160-024-00649-8

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