Tag: University of Illinois at Urbana-Champaign

AI Cracks the Chemistry Code to Better, Longer-lasting Solar Panels

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By integrating AI with automated synthesis, researchers at the University of Illinois significantly enhanced the stability of solar energy molecules, shedding light on the chemical factors influencing photostability. Credit: SciTechDaily.com

Researchers have leveraged artificial intelligence to enhance the photostability of molecules for solar energy applications, achieving molecules four times more stable than previous ones.

Their novel approach involved AI-driven closed-loop experimentation and automated chemical synthesis to uncover the underlying chemical principles of stability, offering fresh insights into molecular design for organic solar cells.

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.

Advancements in Light-Harvesting Molecule Stability

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 today (August 28) 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.”

Illinois researchers have opened up the AI “black box” to gain valuable new insight about chemistry for solar energy applications. Pictured, from left: Professor Charles Schroeder, Changhyun Hwang, Seungjoo Yi, professor Ying Diao, professor Nick Jackson, Tiara Charis, and Torres Flores. Credit: Michelle Hassel

Solving Photostability With Closed-Loop Experimentation

The researchers were motivated by the question of how to improve organic solar cells, which are based on thin, flexible materials, as opposed to the rigid, heavy, silicon-based panels that now dot rooftops and fields.

“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,” said Diao. “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.”

Accelerating Discovery with Modular Chemistry and AI

The Illinois method, called “closed-loop transfer,” begins with an AI-guided optimization protocol called closed-loop experimentation. The researchers asked the AI to optimize the photostability of light-harvesting molecules, Schroeder said. The AI algorithm provided suggestions about what kinds of chemicals to synthesize and explore in multiple rounds of closed-loop synthesis and experimental characterization. After each round, the new data were incorporated back into the model, which then provided improved suggestions, with each round moving closer to the desired outcome.

The researchers produced 30 new chemical candidates over five rounds of closed-loop experimentation, thanks to building block-like chemistry and automated synthesis pioneered by Burke’s group. The work was done at the Molecule Maker Lab housed in the Beckman Institute for Advanced Science and Technology at the 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,” said Burke, who also is 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.”

Unveiling the Secrets of Molecular Stability

Instead of simply ending the query with the final products singled out by the AI, as in a typical AI-led campaign, the closed-loop transfer process further sought to uncover the hidden rules that made the new molecules more stable.

As the closed-loop experiment ran, another set of algorithms was continuously looking at the molecules made, developing models of chemical features predictive of stability in light, Jackson said. Once the experiment concluded, the 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 test their hypothesis about photostability, the researchers investigated three structurally different light-harvesting molecules with the chemical property they identified — a particular high-energy region — and confirmed that choosing the proper solvents made the molecules up to four times more light-stable.

“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.”

Reference: “Closed-loop transfer enables AI to yield chemical knowledge” 28 August 2024, Nature.
DOI: 10.1038/s41586-024-07892-1

This work was supported by the Molecule Maker Lab Institute, an AI Research Institutes program supported by the U.S. National Science Foundation under grant no. 2019897.

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August 28, 2024
New Study Unfolds the Electric Mystery of Peptides

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Electron transport, the energy-generating process inside living cells that enables photosynthesis and respiration, is enhanced in peptides with a collapsed, folded structure. Credit: Moeen Meigooni.

Researchers validated their findings, which were published in PNAS, using a combination of single-molecule experiments, molecular dynamics simulations, and quantum mechanics.

A new study reveals that peptides with a folded structure conduct electricity better than their unfolded counterparts. Researchers at the Beckman Institute used experiments and simulations to demonstrate how these structures influence electron transport, crucial for processes like photosynthesis and respiration. This finding not only deepens our understanding of electron flow in complex molecular structures but also opens new possibilities for developing advanced molecular electronic devices.

What puts the electronic pep in peptides? A folded structure, according to a new study in the Proceedings of the National Academy of Sciences.

Electron transport, the energy-generating process inside living cells that enables photosynthesis and respiration, is enhanced in peptides with a collapsed, folded structure. Interdisciplinary researchers at the Beckman Institute for Advanced Science and Technology combined single-molecule experiments, molecular dynamics simulations, and quantum mechanics to validate their findings.

“This discovery provides a new understanding of how electrons flow through peptides with more complex structures while offering new avenues to design and develop more efficient molecular electronic devices,” said lead investigator Charles Schroeder, the James Economy Professor in Materials Science and Engineering at the University of Illinois Urbana-Champaign.

Proteins reside in all living cells and are integral to cellular activities like photosynthesis, respiration (taking in oxygen and expelling carbon dioxide), and muscle contraction.

Chemically, proteins are long sequences of amino acids strung like holiday lights, the different colors representing different amino acids like tryptophan and glutamine.

In a protein’s simplest form (its primary structure) the amino acid string lies flat. But amino acids are prone to mingling; when they interact with one another, the string tangles, causing the structural collapse referred to as protein folding (or secondary structure).

The researchers asked if and how a protein’s structure impacts its ability to conduct electricity — a question not clearly answered by existing literature.

Research Focus on Peptides

Rajarshi “Reeju” Samajdar, a graduate student in the Schroeder Group, was patiently probing this protein problem by experimenting on one molecule at a time. But Samajdar was not looking at proteins at all. Instead, he focused on peptides, fragments of proteins with a fraction of the amino acids. For this study, Samajdar used peptides with about four or five amino acids, which permitted more granular observation, he said.

Samajdar saw something surprising: stretched-out peptides with a primary structure seemed to be less effective energy conductors than their folded counterparts with a secondary structure. The stark difference between the peptides’ behavior in each state piqued his curiosity.

“Peptides are very flexible. We were interested in understanding how the conductance properties changed as you stretch them out and the peptides transition from a folded secondary structure to an extended conformation. Interestingly, I saw a distinct jump between those two structures, with different electronic properties in each,” Samajdar said.

To verify his observations, Samajdar called on Moeen Meigooni, a graduate research assistant working with Emad Tajkhorshid, a Beckman researcher, professor and the J. Woodland Hastings Endowed Chair in Biochemistry.

The team simulated the peptides’ conformational behavior with computer modeling, confirming the jerky structural shifts Samajdar observed. Leaving no scientific stones unturned, the researchers worked with Martin Mosquera, an assistant professor of chemistry at Montana State University, and Nicholas Jackson, a Beckman researcher and an assistant professor of chemistry at Illinois, to use quantum mechanical calculations to confirm that these two discrete structures were indeed linked to the changes in conductivity.

“We believe that our approach combining single-molecule experiments, structural modeling with molecular dynamics and quantum mechanics is a very powerful approach for understanding molecular electronics,” Samajdar said. “We could have gone straight to quantum, but we didn’t. The computer simulation piece allowed us to study the entire conformational space of the peptides.”

The researchers’ triple-checked results indicate that peptides with a folded secondary structure do conduct electricity better than peptides with an unfolded primary structure. The specific secondary structure they observed formed a shape called the 3₁₀ helix.

Because this work was conducted on peptides, the results lend themselves to a greater understanding of electron transport in larger, more complex proteins and other biomolecules, pointing toward applications in molecular electronic devices like semiconductors that work by switching between two distinct structures.

Reference: “Secondary structure determines electron transport in peptides” by Rajarshi Samajdar, Moeen Meigooni, Hao Yang, Jialing Li, Xiaolin Liu, Nicholas E. Jackson, Martín A. Mosquera, Emad Tajkhorshid and Charles M. Schroeder, 25 July 2024, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2403324121

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July 31, 2024
Sonification Unlocks Protein Folding Pathways

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Through a novel approach using data sonification, researchers have uncovered how hydrogen bonds influence protein folding. This auditory method revealed key patterns and transitions in the folding process, offering insights that surpass visual data analysis and enhancing understanding of diseases linked to protein misfolding. Credit: SciTechDaily.com

Researchers used sound to reveal hidden patterns in protein folding, emphasizing the role of hydrogen bonds and water molecules in shaping protein structures.

Scientists have transformed their data into sounds to uncover how hydrogen bonds contribute to the lightning-fast gyrations that transform a string of amino acids to fold into a functional protein. Their study, published in the Proceedings of the National Academy of Sciences, offers an unprecedented view of the sequence of hydrogen-bonding events that occur when a protein morphs from an unfolded to a folded state.

“A protein must fold properly to become an enzyme or signaling molecule or whatever its function may be — all the many things that proteins do in our bodies,” said University of Illinois Urbana-Champaign chemistry professor Martin Gruebele, who led the new research with composer and software developer Carla Scaletti.

Composer and software developer Carla Scaletti and chemistry professor Martin Gruebele used sound to investigate hydrogen-bond dynamics during the protein-folding process. Credit: Fred Zwicky

Misfolded proteins contribute to Alzheimer’s disease, Parkinson’s disease, cystic fibrosis and other disorders. To better understand how this process goes awry, scientists must first determine how a string of amino acids shape-shifts into its final form in the watery environment of the cell. The actual transformations occur very fast, “somewhere between 70 nanoseconds and two microseconds,” Gruebele said.

A sonification and animation of a state machine based on a simple lattice model used by Martin Gruebele to teach concepts of protein-folding dynamics.

Hydrogen bonds are relatively weak attractions that align atoms located on different amino acids in the protein. A folding protein will form a series of hydrogen bonds internally and with the water molecules that surround it. In the process, the protein wiggles into countless potential intermediate conformations, sometimes hitting a dead-end and backtracking until it stumbles onto a different path.

A sonification and animation of a state machine based on a simple lattice model used by Martin Gruebele to teach concepts of protein-folding dynamics.

The researchers wanted to map the time sequence of hydrogen bonds that occur as the protein folds. But their visualizations could not capture these complex events.

“There are literally tens of thousands of these interactions with water molecules during the short passage between the unfolded and folded state,” Gruebele said.

So the researchers turned to data sonification, a method for converting their molecular data into sounds so that they could “hear” the hydrogen bonds forming. To accomplish this, Scaletti wrote a software program that assigned each hydrogen bond a unique pitch. Molecular simulations generated the essential data, showing where and when two atoms were in the right position in space — and close enough to one another — to hydrogen bond. If the correct conditions for bonding occurred, the software program played a pitch corresponding to that bond. Altogether, the program tracked hundreds of thousands of individual hydrogen-bonding events in sequence.

Video summary for the research “Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification” published in PNAS May 21, 2024 vol. 121 no. 21, DOI: https://doi.org/10.1073/pnas.2319094121

Numerous studies suggest that audio is processed roughly twice as fast as visual data in the human brain, and humans are better able to detect and remember subtle differences in a sequence of sounds than if the same sequence is represented visually, Scaletti said.

“In our auditory system, we’re really very attuned to small differences in frequency,” she said. “We use frequencies and combinations of frequencies to understand speech, for example.”

A protein spends most of its time in the folded state, so the researchers also came up with a “rarity” function to identify when the rare, fleeting moments of folding or unfolding took place.

The resulting sounds gave them insight into the process, revealing how some hydrogen bonds seem to speed up folding while others appear to slow it. They characterized these transitions, calling the fastest “highway,” the slowest “meander,” and the intermediate ones “ambiguous.”

Including the water molecules in the simulations and hydrogen-bonding analysis was essential to understanding the process, Gruebele said.

“Half of the energy from a protein-folding reaction comes from the water and not from the protein,” he said. “We really learned by doing sonification how water molecules settle into the right place on the protein and how they help the protein conformation change so that it finally becomes folded.”

While hydrogen bonds are not the only factor contributing to protein folding, these bonds often stabilize a transition from one folded state to another, Gruebele said. Other hydrogen bonds may temporarily impede proper folding. For example, a protein may get hung up in a repeating loop that involves one or more hydrogen bonds forming, breaking and forming again — until the protein eventually escapes from this cul de sac to continue its journey to its most stable folded state.

“Unlike the visualization, which looks like a total random mess, you actually hear patterns when you listen to this,” Gruebele said. “This is the stuff that was impossible to visualize but it’s easy to hear.”

Reference: “Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification” 20 May 2024, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2319094121

The National Science Foundation, National Institutes of Health and Symbolic Sound Corporation supported this research.

Gruebele also is a professor in the Beckman Institute for Advanced Science and Technology and an affiliate of the Carl R. Woese Institute for Genomic Biology at the U. of I.

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May 20, 2024
Rewriting the Rules of Quantum Storage

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By Amber Rose, University of Illinois Grainger College of Engineering March 14, 2024

Researchers have identified and synthesized a new europium compound, Cs₂NaEuF₆, for quantum memory, utilizing density functional theory calculations. This discovery marks a significant advance in the search for materials capable of storing and transmitting quantum information, with rare earth elements like europium showing particular promise due to their unique atomic structures and long-lived electron excitation states. Credit: SciTechDaily.com

A groundbreaking europium-based compound, Cs₂NaEuF₆, could revolutionize quantum memory storage, indicating a promising direction for quantum computing material research.

In the quest to develop quantum computers and networks, there are many components that are fundamentally different than those used today. Like a modern computer, each of these components has different constraints. However, it is currently unclear what materials can be used to construct those components for the transmission and storage of quantum information.

Discovery of New Quantum Memory Material

In new research published in the Journal of the American Chemical Society, University of Illinois Urbana Champaign materials science & engineering professor Daniel Shoemaker and graduate student Zachary Riedel used density functional theory (DFT) calculations to identify possible europium (Eu) compounds to serve as a new quantum memory platform. They also synthesized one of the predicted compounds, a brand new, air-stable material that is a strong candidate for use in quantum memory, a system for storing quantum states of photons or other entangled particles without destroying the information held by that particle.

“The problem that we are trying to tackle here is finding a material that can store that quantum information for a long time. One way to do this is to use ions of rare earth metals,” says Shoemaker.

The double perovskite crystal structure of Cs₂NaEuF₆ synthesized in this research. Credit: The Grainger College of Engineering at University of Illinois Urbana-Champaign

Rare Earth Elements in Quantum Information

Found at the very bottom of the periodic table, rare earth elements, such as europium, have shown promise for use in quantum information devices due to their unique atomic structures. Specifically, rare earth ions have many electrons densely clustered close to the nucleus of the atom. The excitation of these electrons, from the resting state, can “live” for a long time—seconds or possibly even hours, an eternity in the world of computing. Such long-lived states are crucial to avoid the loss of quantum information and position rare earth ions as strong candidates for qubits, the fundamental units of quantum information.

Challenges and Solutions in Quantum Material Engineering

“Normally in materials engineering, you can go to a database and find what known material should work for a particular application,” Shoemaker explains. “For example, people have worked for over 200 years to find proper lightweight, high-strength materials for different vehicles. But in quantum information, we have only been working at this for a decade or two, so the population of materials is actually very small, and you quickly find yourself in unknown chemical territory.”

Shoemaker and Riedel imposed a few rules in their search of possible new materials. First, they wanted to use the ionic configuration Eu³⁺ (as opposed to the other possible configuration, Eu²⁺) because it operates at the right optical wavelength. To be “written” optically, the materials should be transparent. Second, they wanted a material made of other elements that have only one stable isotope. Elements with more than one isotope yield a mixture of different nuclear masses that vibrate at slightly different frequencies, scrambling the information being stored. Third, they wanted a large separation between individual europium ions to limit unintended interactions. Without separation, the large clouds of europium electrons would act like a canopy of leaves in a forest, rather than well-spaced-out trees in a suburban neighborhood, where the rustling of leaves from one tree would gently interact with leaves from another.

Innovations in Quantum Material Synthesis

With those rules in place, Riedel composed a DFT computational screening to predict which materials could form. Following this screening, Riedel was able to identify new Eu compound candidates, and further, he was able to synthesize the top suggestion from the list, the double perovskite halide Cs₂NaEuF₆. This new compound is air stable, which means it can be integrated with other components, a critical property in scalable quantum computing. DFT calculations also predicted several other possible compounds that have yet to be synthesized.

“We have shown that there are a lot of unknown materials left to be made that are good candidates for quantum information storage,” Shoemaker says. “And we have shown that we can make them efficiently and predict which ones are going to be stable.”

Reference: “Design Rules, Accurate Enthalpy Prediction, and Synthesis of Stoichiometric Eu3+ Quantum Memory Candidates” by Zachary W. Riedel and Daniel P. Shoemaker, 12 January 2024, Journal of the American Chemical Society.
DOI: 10.1021/jacs.3c11615

Daniel Shoemaker is also an affiliate of the Materials Research Laboratory (MRL) and the Illinois Quantum Information Science and Technology Center (IQUIST) at UIUC.

Zachary Riedel is currently a postdoctoral researcher at Los Alamos National Laboratory.

This research was supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Center Q-NEXT. The National Science Foundation through the University of Illinois Materials Research Science and Engineering Center supported the use of facilities and instrumentation.

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March 15, 2024