“There are better uses for a PhD student than waiting in the lab until 3 a.m. to make sure an experiment is run to completion,” says Ant Rostern, ARIA’s chief technology officer.
Arya selected 12 projects to fund from 245 proposals, doubling the amount of funding it allocated due to the large number and high quality of submissions. Half of the teams are from the UK. The rest are from America and Europe. Some teams are from universities, some from industry. Each will receive around £500,000 (about $675,000) to complete 9 months of work. At the end of that time, they should be able to demonstrate that their AI scientist was able to come up with novel results.
Among the winning teams is Leela Sciences, an American company that has developed what it calls AI Nanoscientists, a system that will 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’re using the funds and time to prove a point,” says Rafa Games-Bombarelli at Lila Sciences. “
Another team, from the University of Liverpool, UK, is building a robot chemist that runs multiple experiments at the same time and uses a vision language model to help fix errors when the robot goes wrong.
And an AI scientist called TheWorld, a London-based startup HumansAI, is using the LLM to design experiments to study the physical and chemical interactions that are critical to the performance of batteries. The experiments will then be run in an automated lab by Sandia National Laboratories in the US.
Taking temperature
Compared to the £5 million projects typically fund over 2-3 years, £500,000 is small change. But that was the idea, Rostern says: it’s an experiment on Arya’s part, too. By funding a range of projects for a short period of time, the agency is raising the temperature on the cutting edge to determine how the way science is changing, and how fast. What is learned will become the baseline for financing future large-scale projects.
Rostern acknowledges that there is a lot of hype, especially now that most teams at most AI companies have focused on the science. When results are shared through press releases and not through peer review, it can be difficult to know what the technology can and cannot do. “It’s always a challenge for a research agency trying to fund the frontier,” he says. “To work on the frontier we get to know what the frontier is.”