CHAMPAIGN, Ill. — Artificial intelligence is a powerful tool for researchers, but with a significant limitation: The inability to explain how it came to its decisions, a problem known as the “AI black box.” By combining AI with automated chemical synthesis and experimental validation, an interdisciplinary team of researchers at the University of Illinois Urbana-Champaign has opened up the black box to find the chemical principles that AI relied on to improve molecules for harvesting solar energy.
The result produced light-harvesting molecules four times more stable than the starting point, as well as crucial new insights into what makes them stable — a chemical question that has stymied materials development.
The interdisciplinary team of researchers was co-led by U. of I. chemistry professor Martin Burke, chemical and biomolecular engineering professor Ying Diao, chemistry professor Nicholas Jackson and materials science and engineering professor Charles Schroeder, in collaboration with along with University of Toronto chemistry professor Alán Aspuru-Guzik. They published their results in the journal Nature.
“New AI tools have incredible power. But if you try to open the hood and understand what they’re doing, you’re usually left with nothing of use,” Jackson said. “For chemistry, this can be very frustrating. AI can help us optimize a molecule, but it can’t tell us why that’s the optimum — what are the important properties, structures and functions? Through our process, we identified what gives these molecules greater photostability. We turned the AI black box into a transparent glass globe.”