In the News
AI Ranks Synthesizability of Materials Showing Promise for Carbon Capture
The journal Digital Discovery published a study from an international research team including UMass Amherst chemistry professor Scott Auerbach that applied artificial intelligence (AI) to a long-standing problem in materials science – identifying structures within massive computer-generated databases that are good candidates for actual fabrication. Auerbach and coworkers focused their study on hypothetical zeolites, which show promise for capturing carbon dioxide emissions.
Zeolites are nanoporous crystals that have been utilized for more than 6 decades in a number of industrial processes, particularly in refining petroleum and separating chemical mixtures. While much effort has been put into identifying and synthesizing new zeolites for modern needs such as producing clean biofuels and capturing carbon dioxide, success has been largely theoretical. While massive databases of hypothetical zeolites have been generated containing millions of new framework structures, none has been made in the lab.
“This problem, which is known as the ‘zeolite conundrum,’ has severely limited the pace of the clean energy transition,” said Auerbach. “Finding the few hypothetical zeolites that can actually be synthesized in the lab is like finding a needle in a gigantic haystack.”
Auerbach and coworkers – Michele Ceriotti and Benjamin Helfrecht at the Swiss Federal Institute of Technology (EPFL) in Lausanne, and Rocio Semino and Giovanni Pireddu at the Sorbonne University in Paris – developed an algorithm called the “sorting hat” that uses artificial intelligence and machine learning to distinguish between the 255 already-synthesized zeolites and more than 300,000 hypothetical framework structures. They created a short list of hypothetical zeolites that are so similar to real ones that they are “misclassified” by the sorting hat as real materials – making them good candidates for actual synthesis.
“Ours is the first study to apply AI to the zeolite conundrum, “ said Auerbach. “All the previous studies are biased by preconceived ideas about what makes real zeolites real – we wanted to move away from such bias.”
After filtering their results by additional criteria, including the potential for stabilizing them during synthesis, the researchers proposed three leading hypothetical candidates for synthesis. Their analysis also categorized real zeolites into four compositional classes or “houses.” This partitioning into houses allowed the researchers to propose chemical compositions to pursue in the laboratory for making the hypothetical zeolites – like recipes for synthesis.
“As is the case for many synthetic tasks, making zeolites is a form of art, guided by experience, chemical intuition and serendipity,” the researchers said. “The zeolite sorting hat introduces data-driven techniques and rational design into the process of selecting candidates that we hope will accelerate the rate of discovery that will in turn, will improve the predictive capabilities of the model in a positive feedback mechanism that will progressively take the guesswork out of zeolite synthesis.”
Faculty Search - Assistant Professor in Chemistry - DNA/RNA/Biologics
Kittilstved Selected as UMass ADVANCE Faculty Fellow
Rotello Receives the Arthur C. Cope Scholar Award
An interdisciplinary team of UMass Amherst researchers had their recently published article chosen as a “hot” article in the journal Physical Chemistry Chemical Physics. The team, led by chemistry professor Scott Auerbach and chemical engineering professor Wei Fan, reported breakthrough computer simulations confirmed by experiments showing faster crystallization of nanoporous catalysts known as zeolites.
“Understanding how to make zeolites, and how to make them faster, is incredibly important. Zeolites are the most used synthetic catalysts on planet earth, and they show great potential for making green fuels and capturing carbon dioxide – both critical for battling climate change,” said Dr. Auerbach.
The team also includes lead author Dr. Cecilia Bores, a former postdoctoral fellow at UMass Amherst and now a physics professor at Union College, as well as chemical engineering PhD student Song Luo and undergraduate researchers J. David Lonergan, Eden Richardson, and Alexander Engstrom.
“Simulating zeolite crystallization is one of the grand challenges in materials science because the process can take days to weeks, so our simulations have to efficiently model very slow assembly processes,” said Dr. Bores. She continued, “The key to our work is capturing only the essential aspects of zeolite bonding and intentionally omitting some interactions between particles that would only slow down the simulation.”
Also critical to the work are experimental tests confirming that the simulation predictions are correct. Such experiments, carried out by Fan and Luo, involve using additives called “structure directing agents” to help steer the crystallization. Fan and Luo confirmed the prediction that using multiple structure directing agents that match the different nanopore sizes within a zeolite can speed up crystallization, by as much as a factor of three.
“Learning how faster zeolite crystallization occurs by using several structure directing agents is a real breakthrough for my lab,” said Dr. Fan. “We spend countless hours trying to fabricate new zeolites, so being able to speed up the process can lead to much faster discovery of new and useful materials.”
The team plans to continue the research, which is funded by the Department of Energy’s program in Synthesis and Processing Science, by applying artificial intelligence to analyze the simulated crystallization trajectories to identify key steps that lead to crystals, and by testing those predictions using advanced experimental methods such as Raman spectroscopy.
“Being able to combine computer simulations with experiments so seamlessly is critical to this research,” said Dr. Auerbach. “Our collaboration with Wei Fan and his team has been fantastic. As we like to say: ‘Without Wei, there’s no way!’”
Journal Dedicated to Emeritus Raymond Barnes
Sun Receives Paul Hatheway Terry Scholarship
Zhining (Jennings) Sun received Paul Hatheway Terry Scholarship in recognition of excellence in research. Research Summary: Genetically encodable RNA-based fluorescent sensors have been a revolutionary tool for real-time imaging of important biological small molecules in live cells. Guanosine tetraphosphate (also known as ppGpp or “Magic Spot”) in particular is one of the targets that plays an integral role in cell regulation. Its presence in bacteria cells triggers the stringent response which helps the cells to survive the harsh living conditions via various pathways. Although many researches have been done to study its functions, people still have not been able to fully understand it due to the lack of tools to monitor it in live cells. I engineered a naturally occurring ppGpp riboswitch into an RNA-based fluorescent sensor and achieved imaging of ppGpp in live E. coli cells. After half a century since its discovery, we are the first group to ever visualize ppGpp and provide information on its cellular dynamics and cell-to-cell variations. Now I’m working on the multiplex imaging project to study ppGpp and other related targets simultaneously, which will discover the potential correlation between the targets as well as how they affect the cell biology.
Zhou Lin, assistant professor in chemistry, and her co-authors received the (“science + dialog") Scialog Award for their proposal to develop a new electrosynthetic route that reduces the emissions of two most significant greenhouse gases from waste management and treatment activities, carbon dioxide, and methane. The $55,000 grant will help the team design unconventional electrochemical reactors and catalysts to enable direct coupling of carbon dioxide and methane into valuable liquid feedstock.
Jianhan Chen, a University of Massachusetts Amherst chemistry and biochemistry and molecular biology professor, has received a five-year, $2 million National Institutes of Health (NIH) grant to support research in his computational biophysics lab aimed at better understanding the role of intrinsically disordered proteins (IDPs) in biology and human disease.
The grant falls under the National Institute of General Medical Sciences MIRA program, which stands for Maximizing Investigators’ Research Award. It’s designed to give highly talented researchers more flexibility and stability to achieve important scientific advances in their labs.
“The MIRA award enables us to continue working on several central problems regarding the study of disordered proteins and dynamic interactions. The flexibility of this funding mechanism also allows us to follow new research directions as they emerge,” Chen says.
Until relatively recently, it was thought that proteins needed to adopt a well-defined structure to perform their biological function. But about two decades ago, Chen explains, IDPs were recognized as a new class of proteins that rely on a lack of stable structures to function. They make up about one-third of proteins that human bodies make, Chen explains, and two-thirds of cancer-associated proteins contain large, disordered segments or domains.
“This disorder seems to provide some unique functional advantage, and that’s why we have so much disorder in certain kinds of proteins,” Chen says. “These IDPs play really important roles in biology, and when something breaks down, they lead to very serious diseases, like cancers and neurodegenerative diseases.”
In his lab, Chen and colleagues focus on using computer simulations to model the molecular structure and dynamics of proteins. “IDPs are a mess; it’s difficult to determine the details of their properties because they are not amenable to traditional techniques that are designed to resolve stable protein structures,” he says.
Because of their chaotic state, IDPs must be described using ensembles of structures, and computer simulations play a crucial role in the quantitative description of these disordered ensembles. “Our goal is really trying to combine simulation and experiments in collaboration with other labs to tease out what are the hidden features of these disordered proteins that are crucial to their function,” Chen says. “Then we can look at how these specific features might be perturbed by disease-related mutations or conditions.”
The next step would be to develop effective strategies for targeting disordered protein states. Toward that end, Chen’s lab will study the molecular basis of how the anti-cancer drug EGCG, an antioxidant found in green tea extract, and their derivatives interact with the p53 gene, a tumor suppressor and the most important protein involved in cancer.
The key, he says, is knowing how to design drug molecules to bind well enough to IDPs to achieve a therapeutic effect. Traditional, structure-based drug design strategies are faced with significant challenges, Chen says, because IDPs do not contain stable, “druggable” pockets.
“We believe that targeting IDPs requires new strategies that explore the dynamic nature of IDP interactions,” Chen says. “If we can do this, it could really open up a whole class of drugs that were previously thought impossible.”