The team's approach resulted in light-harvesting molecules that are four times more stable than the original versions. Moreover, the research yielded new insights into the chemical factors that contribute to this stability, addressing a long-standing challenge in materials development.
This groundbreaking research was a collaborative effort 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, alongside University of Toronto chemistry professor Alan Aspuru-Guzik. The findings were published 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."
The research was driven by the need to improve organic solar cells, which utilize flexible, lightweight materials as opposed to traditional rigid, heavy silicon-based panels.
"What has been hindering commercialization of organic photovoltaics is problems with stability. High-performance materials degrade when exposed to light, which is not what you want in a solar cell," Diao explained. "They can be made and installed in ways not possible with silicon and can convert heat and infrared light to energy as well, but the stability has been a problem since the 1980s."
The method developed by the Illinois team, known as "closed-loop transfer," starts with an AI-guided optimization protocol called closed-loop experimentation. The AI was tasked with improving the photostability of light-harvesting molecules. In each round of closed-loop synthesis and experimental characterization, the AI provided new chemical candidates to explore. The data from these experiments were fed back into the model, refining the AI's suggestions until the desired outcome was achieved.
Over five rounds of closed-loop experimentation, the team produced 30 new chemical candidates, utilizing building block-like chemistry and automated synthesis methods developed by Burke's group at the Molecule Maker Lab, located within the Beckman Institute for Advanced Science and Technology at U. of I.
"The modular chemistry approach beautifully complements the closed-loop experiment. The AI algorithm requests new data with maximized learning potential, and the automated molecule synthesis platform can generate the new required compounds very quickly. Those compounds are then tested, the data goes back into the model, and the model gets smarter-again and again," Burke said, who is also a professor in the Carle Illinois College of Medicine. "Until now, we've been largely focused on structure. Our automated modular synthesis now has graduated to the realm of exploring function."
Instead of simply identifying the final products as in a typical AI-led campaign, the closed-loop transfer process also sought to reveal the underlying rules that contributed to the improved stability of the new molecules.
As the closed-loop experiment proceeded, another set of algorithms continuously analyzed the generated molecules, developing models to predict chemical features associated with stability in light. Once the experiment was complete, these models provided new, lab-testable hypotheses.
"We're using AI to generate hypotheses that we can validate to then spark new human-driven campaigns of discovery," Jackson said. "Now that we have some physical descriptors of what makes molecules photostable, that makes the screening process for new chemical candidates dramatically simpler than blindly searching around chemical space."
To validate their hypothesis on photostability, the researchers tested three different light-harvesting molecules with the identified chemical property-a specific high-energy region-and confirmed that selecting the appropriate solvents could enhance their light stability by up to four times.
"This is a proof of principle for what can be done. We're confident we can address other material systems, and the possibilities are only limited by our imagination. Eventually, we envision an interface where researchers can input a chemical function they want and the AI will generate hypotheses to test," Schroeder said. "This work could only happen with a multidisciplinary team, and the people, resources, and facilities we have at Illinois, and our collaborator in Toronto. Five groups came together to generate new scientific insight that would not have been possible with any one of the sub teams working in isolation."
Research Report:Closed-loop transfer enables AI to yield chemical knowledge
Related Links
Materials Science And Engineering at IllinoisU
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