A firm randomly assigned its scientists AI: here’s what happened
Scientists at an unnamed corporate laboratory were randomly assigned a machine-learning tool.Credit: Eugenio Marongiu/Getty
Artificial intelligence (AI) is becoming ubiquitous in applied research, but can it actually invent useful materials faster than humans can? It is still too early to tell, but a massive study suggests that it might.
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Aidan Toner-Rodgers, an economist at the Massachusetts Institute of Technology (MIT) in Cambridge, followed the deployment of a machine-learning tool at an unnamed corporate laboratory employing more than 1,000 researchers. Teams that were randomly assigned to use the tool discovered 44% more new materials and filed 39% more patent applications than did the ones that stuck to their standard workflow, he found. Toner-Rodgers posted the results online last month, and has submitted them to a peer-reviewed journal.
“It is a very interesting paper,” says Robert Palgrave, a solid-state chemist at University College London, adding that the limited disclosure of the trial’s details makes the results of the AI deployment hard to evaluate. “It maybe doesn’t surprise me that AI can come up with a lot of suggestions,” Palgrave says. “What we’re kind of missing is whether those suggestions were good suggestions or not.”
Materials maker
Toner-Rodgers had access to internal data from the lab and interviewed the researchers under the condition that he would not disclose the name of the company or the specific products it designed. He writes that it is a US firm that develops new inorganic materials — including molecular compounds, crystal structures, glasses and metal alloys — for use in “healthcare, optics, and industrial manufacturing”.
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Starting in 2022, the company systematically adopted an AI tool that it had customized to fit its needs. According to Toner-Rodgers, the tool combines graph neural networks — a popular approach in materials discovery that has been used by DeepMind, Google’s London-based AI firm, among others — with reinforcement learning. The neural network was pre-trained using data from vast existing databases, including crystal structures and their properties from the Materials Project and molecular structures from the Alexandria Materials Database.
Researchers input requirements for a material’s desired properties into the neural network, and the system suggests structures for new materials that could have those properties. The teams then weed out potential duds — such as formulas that would not lead to a stable compound — using their own specialist knowledge and computer simulations. They then attempt to synthesize the candidate structures and, if successful, test them in experiments and even in prototypes of finished products. The results are fed back into the neural network — the ‘reinforcement’ stage that helps it to improve its predictive abilities.
