“There are better uses for a PhD student than waiting around in a lab until 3am to make sure an experiment is run to the end,” says Ant Rowstron, ARIA’s chief technology officer.
ARIA picked 12 projects to fund from the 245 proposals, doubling the amount of funding it had intended to allocate because of the large number and high quality of submissions. Half the teams are from the UK; the rest are from the US and Europe. Some of the teams are from universities, some from industry. Each will get around £500,000 (around $675,000) to cover 9 months’ work. At the end of that time, they should be able to demonstrate that their AI scientist was able to come up with novel findings.
Winning teams include Lila Sciences, a US company that is building what it calls an AI NanoScientist, a system that will design and run experiments to discover the best ways to compose and process quantum dots, which are nanometer-scale semiconductor particles used in medical imaging, solar panels and QLED TVs.
“We are using the funds and time to prove a point,” says Rafa Gómez-Bombarelli at Lila Sciences: “The grant lets us design a real AI robotics loop around a focused scientific problem, generate evidence that it works, and document the playbook so others can reproduce and extend it.”
Another team, from the University of Liverpool, UK, is building a robot chemist, which runs multiple experiments at once and uses a vision language model to help troubleshoot when the robot makes an error.
And Humanis AI, a startup based in London, is developing an AI scientist called ThetaWorld, which is using LLMs to design experiments to study the physical and chemical interactions that are important for the performance of batteries. The experiments will then be run in an automated lab by Sandia National Laboratories in the US.
Taking the temperature
Compared to the £5 million projects spanning 2-3 years that ARIA usually funds, £500,000 is small change. But that was the idea, says Rowstron: It’s an experiment on ARIA’s part too. By funding a range of projects for a short amount of time, the agency is taking the temperature at the cutting edge to determine how the way science is done is changing, and how fast. What it learns will become the baseline for funding future large-scale projects.
Rowstron acknowledges there’s a lot of hype, especially now that most of the top AI companies have teams focused on science. When results are shared by press release and not peer review, it can be hard to know what the technology can and can’t do. “That’s always a challenge for a research agency trying to fund the frontier,” he says. “To do things at the frontier we’ve got to know what the frontier is.”