Medical technology: is the future self-driving laboratories?

Medical technology: is the future self-driving laboratories?

IDTechEx has identified three core technology pillars that are required for the advent of self-driving laboratories, including laboratory informatics, materials informatics, and robotics.

Self-driving laboratories, whereby a lab automatically chooses what experiments to do, robotically carries this out, tracks the reaction with integrated sensors, acquires and analyses the results, and then decides what experiment to do next, may be far from being achieved, however, IDTechEx has highlighted how technological innovations that can drive their creation are already here.

IDTechEx has released a detailed report about materials informatics, detailing the key technologies, players, applications, and market outlook.

Laboratory informatics

The role of laboratory informatics and robotics can take numerous forms, including well known high-throughput experimentation through to full digital platforms and integrated sensors to monitor experiments.

These developments are having an immediate impact on the reproducibility, capacity to internally share, safety, and rate of generating experimental data.

Materials informatics (or cheminformatics or bioinformatics as appropriate) plays a key role in each stage of the experimental cycle. From candidate screening and retrosynthetic predictions through to structure-property relations and further analysis, the impact this can have on a closed-loop laboratory process is evident.

Work from the Harvard University, University of Toronto, and the University of Glasgow are some of the key institutes in this field, with Kebotix and DeepMatter Group being exciting spinouts commercialising these developments.

Current advancements

Current examples of advancements made towards self-driving laboratories include one early study which was demonstrated by the US Air Force Research Laboratory in collaboration with Lockheed Martin. By combining high-throughput CVD synthesis of SWCNTs with Artificial Intelligence (AI)-led techniques, they created an Autonomous Research System (ARES), demonstrating that the system could learn to optimise the growth of nanotubes by controlling various experimental parameters.

Further, in 2020 the North Carolina State University and the University at Buffalo showed a proof-of-concept in which an appropriate quantum dot could be identified and produced in less than 15 minutes for any colour. Similarly, work from the University of Glasgow explored coordination chemistry through the discovery of new supramolecular complexes with an autonomous chemical robot.

For more information on this report, please visit

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