By navigating our site, you agree to allow us to use cookies, in accordance with our Privacy Policy.

IDTechEx Publishes report on Self Driving Labs

IDTechEx has released a detailed market report on materials informatics detailing the key technologies, players, applications, and market outlook. Through extensive primary interviews and leveraging deep knowledge in relevant vertical sectors, this report provides the reader with a detailed understanding of the commercial potential in this field.

IDTechExIDTechEx has identified three core technology pillars that are required. Laboratory informatics, materials informatics (or cheminformatics/bioinformatics as appropriate), and robotics.

The role of laboratory informatics and robotics can take numerous forms. This can include well known high-throughput experimentation through to full digital platforms and integrated sensors to monitor experiments. Stepping away from the potential human-less end goal, these developments are having an immediate impact on the reproducibility, capacity to internally share, safety, and rate of generating experimental data.

Materials informatics (or chem.-informatics or bioinformatics as appropriate) plays a key role in each stage of the experimental cycle. Previous articles have gone into detail about how this can benefit, but from candidate screening and retro synthetic 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 commercializing these developments. 

There are already notable examples of early versions of this final goal. One early study was demonstrated by the US Air Force Research Laboratory in collaboration with Lockheed Martin. By combining high-throughput CVD synthesis of SWCNTs with AI-led techniques, they created an Autonomous Research System (ARES). They demonstrated that the system could learn to optimize the growth of nano-tubes by controlling various experimental parameters. There has been a recent acceleration in demonstrations, 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 color. Similarly, work from the University of Glasgow explored coordination chemistry through the discovery of new supramolecular complexes with an “autonomous chemical robot.”


Aishwarya Saxena

A book geek, with creative mind, an electronics degree, and zealous for writing.Creativity is the one thing in her opinion which drove her to enter into editing field. Allured towards south Indian cuisine and culture, love to discover new cultures and their customs. Relishes in discovering new music genres.

Related Articles

Upcoming Events