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Laboratory automation can take repetitive adjustments out of a chemist’s day without automating scientific judgment. A 2017 demonstration by researchers at Imperial College London and the University of Southampton used software to turn a researcher-defined optimisation goal and reaction limits into instructions for a feedback-controlled reactor. The system adjusted conditions in response to analytical results; researchers still chose the goal and designed the experiment.
What “taking labour out” meant in the 2017 report
In Hugh Cowley’s Chemistry World report, published on 29 September 2017, John de Mello and colleagues described free software intended to remove routine manual work from reaction optimisation. The researcher supplied the objective and bounds on reaction conditions; the software translated those requirements into instructions for an automated system.
This was task-level automation, not a claim that a machine could independently decide what chemistry to investigate. The people involved still defined the problem and its constraints. As de Mello put it, “What we’re trying to do is get rid of some of the routine manual work.”
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The report described an optimisation experiment involving sequential Diels–Alder additions of aromatic dienes to C60. In-line chromatography provided measurements to a reactor setup, which adjusted temperature, reaction time and reactant ratio to maximise the desired singly- and doubly-functionalised products.
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The significance of the feedback loop is that the system could use measured outcomes to guide subsequent condition adjustments, rather than simply execute a fixed recipe. The report connected the target products with possible light-harvesting and electronics applications, noting that additional functionalisation could hinder those properties. It cited a 2017 paper by B. E. Walker and colleagues in Reaction Chemistry & Engineering (DOI: 10.1039/C7RE00123A). The description here follows the report; the report is not a substitute for the original paper when exact experimental parameters are needed.
What automation can—and cannot—take off a researcher’s plate
A feedback-controlled setup can handle repeated condition adjustments and measurements within a defined optimisation task. That can free researchers to focus more on reaction design and other conceptual questions. It does not eliminate the need for chemists to select meaningful objectives, interpret results or decide what to investigate next.
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Nor does automating a reactor make the analytical side effortless. Cowley’s report identified instrumentation as a major practical constraint: analytical instruments were often many times more expensive than the reactor systems they monitored. It gave no specific price or ratio, so that comparison should be understood as qualitative, not as a cost estimate.
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De Mello also pointed to the limits of screening with existing instrumentation, saying he wanted to explore alternatives that could screen parameter spaces more quickly and support more sophisticated multistep reactions. Faster measurement can matter because optimisation depends on obtaining useful feedback as experiments proceed.
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How this fits into today’s self-driving laboratories
The 2017 system is one historical example, not evidence that the same software remains available or maintained today. A 2025 review of autonomous “self-driving” laboratories describes a broader field combining laboratory automation with AI. That wider category should not be confused with the specific software and apparatus in Cowley’s report.
Automation can reduce human effort on tasks that are readily automated, but these systems also need people to develop, integrate, maintain and service them. The 2025 review notes that recent studies specifically examining effects on scientific and engineering workforces are limited. The available evidence therefore supports a shift in the kinds of work people do—not a conclusion that scientists will broadly lose their jobs.
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What to consider when automating a chemistry workflow
The 2017 account and later discussion suggest that the useful question is not simply whether to automate, but which task can be automated and what the feedback loop requires.
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- Define the task: Identify the repeated operation or optimisation step, and specify the desired outcome and permissible condition ranges.
- Plan for measurement: Choose analytical feedback capable of producing information useful to the control system. In-line analysis was central to the reported demonstration.
- Account for integration and upkeep: Reactor control, analytical instruments and software must work together, and people are still needed to maintain and service the setup.
- Weigh equipment needs: The 2017 report cautioned that analytical instrumentation can be a significant cost relative to the reactor itself; it did not provide figures for estimating a particular project’s budget.
- Keep scientific oversight: Automation can carry out bounded adjustments, while researchers remain responsible for the scientific objective and interpretation.
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