Self-Driving Laboratories Do Research On Autopilot

Scientific research is a messy business. The road to learning new things and making discoveries is paved with hard labor, tough thinking, and plenty of dead ends. It’s a time-consuming, expensive endeavor, and for every success, there are thousands upon thousands of failures.

It’s a process so inefficient, you would think someone would have automated it already. The concept of the self-driving laboratory aims to do exactly that, and could revolutionize materials research in particular.

Leave It To The Auto-Lab

Materials research is a complex field and a challenging one to work in. Much of the work involves finding improvements on existing materials to make them harder, better, faster, or stronger. There’s also the scope to discover entirely new materials with unique properties and capabilities beyond those already known to us.

In the modern world, it’s not enough to just go outside and dig up a new kind of rock or find a new kind of tree. All the low-hanging fruit are already gone. Materials research now requires a sound understanding of physics and chemistry. It’s all about figuring out how to exploit those principles to make something better than what we’ve seen before.

This is where artificial intelligence and computers come in. Rules we’ve discovered in chemistry and physics can be programmed into an intelligent system. It’s then a straightforward leap to have that system apply those rules in varying ways to optimize desired outcomes. For example, an AI system can be asked to synthesize a given chemical in the most efficient way possible given a certain set of precursor chemicals. It’s then possible for the AI to run through all the possibilities and determine the best course of action.

Where the concept gets most compelling, though, is where an AI system is given the capacity to run its own experiments in the real world. Laboratory automation is advanced to the point where robots can readily run experiments far quicker and more efficiently than human scientists can do by hand. Give the AI the hardware to do experiments and to measure the results of its work, and it can then use the results to guide further experiments towards its given research goals. Congratulations – you’ve built a self-driving laboratory!

In Practice

Far from a mere theory, researchers around the world are already building self-driving laboratory systems. One of the most well-known is the so-called Artificial Chemist, developed by researchers at the University of Buffalo and North Carolina State University. The project’s goal was to develop an automated system to perform chemistry research for the research and development of commercially-desirable materials.

It’s designed to perform chemical research into materials that can be made using liquid solutions. The system is tasked with finding a way to synthesize a material that meets a set of desired parameters, and performs experiments on its own to determine how to achieve that. In testing the system was tasked with synthesizing quantum dots with various desired parameters. Through experimentation, Artificial Chemist was able to figure out ideal techniques on how to make the dots, including the identification of the correct chemical precursors.

Far from a simple computer simulation, Artificial Chemist does real chemistry on its own, and measures the results. The system was outfitted with chemical reactors that are entirely autonomous. They’re also designed to remain clean without picking up chemical residues that would throw off the experiments. The system can mix chemicals and run an entire chemical synthesis all on its own.

The system was developed with an eye to both research and manufacturing. It can be tasked to produce quantum dots for a given wavelength of light, and will first spend time doing research experiments to determine the best way to make them. Once that process is complete, usually after 1-10 hours, the system can then begin producing the dots en masse.


Overall, though, the basic principle can be applied to all kinds of research processes. One need only give a suitable AI system the means to experiment and the means to examine the results of its work. It can then take the logical steps to further its work in the direction of its given research goals.

The benefits of such systems are manifold. Where parts of experiments may have been automated by robots before, self-driving laboratories go further. They enable scientists to set a goal and the automated lab works its way to a solution entirely indepdently. This enables research to be carried out with less labor and human effort, with progress made far faster and far cheaper than before. Plus, the ability for quick calculation and experimentation may allow an AI to quickly run tests on combining regular ingredients in unexpected ways, netting surprise unconventional results. Some researchers expect these systems to provide a tenfold benefit to costs and time, where goals that once took ten years and $10 million dollars completed in one year and for just $1 million.

Of course, such systems won’t make human researchers obsolete. Creativity is of huge importance in science and engineering disciplines, and has led to some of our biggest advances. For example, an AI could be tasked to make stronger and more lightweight metal alloys. However, given those human-spawned preconceptions, it would never come up with the brilliance of composite materials like carbon fiber.

A great corollary is the image synthesis AIs which have skyrocketed in popularity this year. Initially, hyperbole stated that artists and photographers would be out of a job and human endeavour in this field was over. Then, weeks later, it turned out that these were just a new kind of tool that could be guided and put to work by humans best experienced in exploiting them.

These “self-driving laboratories” will likely become major tools in industrial R&D labs, doing everything from developing new materials to uncovering new molecules of potential medical interest. Talented research scientists will work to best employ the robotic resources they have, ensuring they’re put to work in the most effective manner for their broader research goals in general. With much of the research drudgery handed off to the robots, that will leave human scientists more time to think about the bigger picture.

Banner image: © xiaolangge / Adobe Stock.

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