Researchers used artificial intelligence to improve the photostability of molecules for solar energy applications and to obtain molecules that are four times more stable than previous ones.
Their novel approach involved AI-driven closed-loop experiments and automated chemical synthesis to uncover the underlying chemical principles of stability and provide new insights into the molecular design of organic solar cells.
Artificial intelligence is a powerful tool for researchers, but it has a significant limitation: It cannot explain how it arrives at 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 the black box and discovered the chemical principles that AI relied on to improve molecules for solar energy harvesting.
Advances in the stability of light-harvesting molecules
The result was light-harvesting molecules four times more stable than the starting point and provided important new insights into what makes them stable – a chemical question that has hampered materials development so far.
The interdisciplinary research team was co-led by Martin Burke, professor of chemistry at the University of Toronto, Ying Diao, professor of chemical and biomolecular engineering, Nicholas Jackson, professor of chemistry and materials science and engineering, Charles Schroeder, in collaboration with Alán Aspuru-Guzik, professor of chemistry at the University of Toronto. They published their findings today (28 August) in the journal Nature.
“New AI tools are incredibly powerful. But when you try to open the hood and understand what they’re doing, you’re usually left with nothing useful,” Jackson said. “In chemistry, that 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’ve figured out what gives these molecules greater photostability. We’ve turned the AI black box into a transparent glass ball.”
Solving photostability through closed-loop experiments
The researchers were motivated by the question of how to improve organic solar cells based on thin, flexible materials, as opposed to the rigid, heavy silicon-based panels that adorn roofs and fields today.
“What is hindering the commercialization of organic photovoltaics are stability issues. High-performance materials degrade when exposed to light, which is not what is wanted in a solar cell,” Diao said. “They can be manufactured and installed in ways that are not possible with silicon, and can also convert heat and infrared light into energy, but stability has been a problem since the 1980s.”
Accelerated discoveries with modular chemistry and AI
The Illinois method, called “closed-loop transfer,” begins with an AI-driven optimization protocol called a closed-loop experiment. The researchers asked the AI to optimize the photostability of light-harvesting molecules, Schroeder said. The AI algorithm provided suggestions about what types of chemicals to synthesize and study in multiple rounds of closed-loop synthesis and experimental characterization. After each round, the new data was integrated back into the model, which then provided improved suggestions, with each round getting closer to the desired outcome.
The researchers produced 30 new chemical candidates in five rounds of closed-loop experimentation, thanks to building block chemistry and automated synthesis developed by Burke’s group. The work was carried out in the Molecule Maker Lab of the Beckman Institute for Advanced Science and Technology at the University of Illinois.
“The modular chemistry approach beautifully complements the closed-loop experiment. The AI algorithm requests new data with maximum learning potential, and the automated molecule synthesis platform can generate the new required compounds very quickly. These compounds are then tested, the data flows back into the model, and the model gets smarter and smarter – over and over again,” said Burke, who is also a professor at Carle Illinois College of Medicine. “Until now, we have largely focused on structure. Our automated modular synthesis has now entered the realm of functional exploration.”
Unlocking the secrets of molecular stability
Furthermore, rather than simply ending the query with the final products picked out by the AI, as is the case with a typical AI-led campaign, the closed-loop transfer process aimed to uncover the hidden rules that made the new molecules more stable.
While the closed-loop experiment was running, another set of algorithms continuously monitored the resulting molecules and developed models of chemical properties that predict stability in the light, Jackson said. After the experiment was complete, the models provided new hypotheses that could be tested in the lab.
“We’re using AI to generate hypotheses that we can validate to then drive new human-led discovery campaigns,” Jackson said. “Now that we have some physical descriptions of what makes molecules photostable, the screening process for new chemical candidates is much simpler than searching blindly in chemical space.”
To test their photostability hypothesis, the researchers studied three structurally different light-harvesting molecules with the chemical property they identified – a specific high-energy range – and confirmed that choosing the right solvents made the molecules up to four times more photostable.
“This is a proof of concept. We are confident that we can look at other material systems as well, and the possibilities are only limited by our imagination. Ultimately, we envision an interface where researchers can input a desired chemical function and the AI will generate hypotheses to test,” said Schroeder. “This work could only be done with a multidisciplinary team and the people, resources and facilities we have in Illinois and at our collaborator in Toronto. Five groups have come together to generate new science that would not have been possible with any of the subteams working in isolation.”
Reference: “Closed-loop transfer enables AI to gain chemical knowledge” August 28, 2024, Nature.
DOI: 10.1038/s41586-024-07892-1
This work was supported by the Molecule Maker Lab Institute, a program of the AI Research Institutes funded by the U.S. National Science Foundation under grant number 2019897.
