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Laboratory automation can take repetitive reaction adjustments off a researcher’s hands without automating scientific judgment. A 2017 Chemistry World report described software that turned a chemist’s optimisation goal and operating limits into instructions for a feedback-controlled reactor. In its demonstration, analytical measurements guided changes to reaction conditions; people still set the goal and designed the research.
What the software automated
John de Mello and colleagues at Imperial College London and the University of Southampton developed free research software that translated two human inputs—a target for reaction optimisation and bounds on the conditions—into instructions for an automated system. The point was to automate a bounded experimental task, not to have software decide what scientific question to ask.
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As de Mello put it in the 2017 report, “What we’re trying to do is get rid of some of the routine manual work.” That distinction matters: handing over repeated adjustments can free researchers to spend more time on reaction design and other conceptual work, but does not remove the need for chemists.
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How the demonstration used analytical feedback
The report described an optimisation involving sequential Diels–Alder addition of aromatic dienes to C60. In-line chromatography provided measurements to a reactor setup, which then adjusted temperature, reaction time and reactant ratio to maximise the desired singly- and doubly-functionalised products. The report connected those materials with potential uses in light harvesting and electronics, where additional functionalisation could hamper the properties of interest.
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This was an example of a feedback loop: measure the reaction, use the result to guide the next conditions, and repeat toward the defined target. The account does not establish that the whole research process was autonomous, nor does it provide exact optimisation results or operating parameters. The underlying paper is B. E. Walker et al., Reaction Chemistry & Engineering (2017), DOI 10.1039/C7RE00123A.
Why analysis can be a bottleneck
Automation depends on measurements that arrive in a form and at a pace the control system can use. In the report, analytical instrumentation was a significant practical cost: it was described as often many times more expensive than the reactor systems being monitored. No specific price or cost ratio was given, so the comparison should be read as a qualitative warning rather than a budget estimate.
De Mello also pointed to the need for instrumentation that could screen parameter spaces more quickly, particularly for more sophisticated multistep reactions. In practice, an automation project therefore has to consider the analytical method alongside the reactor: what it can measure, how quickly it returns useful feedback, and how readily it can be integrated with the control system.
What this means for modern self-driving laboratories
The 2017 report is a historical account of one software effort and demonstration, not evidence that the same software or apparatus is currently available or maintained. A broader 2025 review describes self-driving laboratories as combinations of AI and laboratory automation. It notes that automation can reduce human labour for tasks suited to it, while the systems themselves require people to develop, maintain and service them. The review also says recent studies specifically examining effects on scientific and engineering workforces are limited, so broad predictions about scientists losing jobs are not established.
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The practical value is narrower and more useful: automation can shift time away from routine operations and toward work that still calls for scientific expertise. Whether a system is worthwhile depends on the task, the feedback and analytical requirements, the integration effort, desired throughput, and equipment costs—not on the label “self-driving” alone.
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Sources
- Hugh Cowley, “Taking labour out of the laboratory,” Chemistry World, 29 September 2017. The historical details and quotations above reflect that report.
- “Autonomous ‘self-driving’ laboratories: a review of technology and policy implications,” 2025 review hosted by PubMed Central and published by the Royal Society: https://pmc.ncbi.nlm.nih.gov/articles/PMC.
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