Physical Chemistry Archives

UT Chemistry Lab Explores Dipeptides for Carbon Dioxide Capture

March 11, 2025 by Jennifer Brown

Vogiatzis’ publication was featured on the cover of the journal ChemPhysChem.

Associate Professor Konstantinos Vogiatzis’ lab in the Department of Chemistry is leveraging computational chemistry to address excess carbon dioxide (CO₂) in the atmosphere.

The presence of excess CO₂ in the atmosphere is believed to have a number of far-reaching impacts on the environment. Over the last 60 years the amount of CO₂in the atmosphere has more than tripled. Today, carbon dioxide levels are estimated to be higher than ever before in human history. The presence of such high levels of CO₂ in the atmosphere is believed to have a number of far-reaching impacts on the environment.

One common method of managing excess CO₂ is carbon capture and storage (CCS). CCS usually employs amine-based solvents to trap CO₂ and prevent it from moving into the atmosphere. However, this method has some limitations. The solvents used in this process are expensive, volatile, and can produce harmful byproducts that may increase cancer risks in humans.

Seeking a more sustainable solution, Vogiatzis, graduate student Amarachi Sylvanus, and post-doctoral researcher Grier Jones explored dipeptides as a natural, bioinspired alternative for CO₂ sequestration. This work was done in collaboration with Radu Custelcean, distinguished research scientist at Oak Ridge National Laboratory.

The research team generated a database of 960 dipeptide molecules derived from 20 natural amino acids and developed an automated workflow to model molecular interactions with CO₂.

By leveraging density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT), they systematically evaluated interactions between the dipeptides and CO₂. Their analysis identified key amino acid subunits that enhance CO₂ binding through cooperative effects.

“Our results confirm that cooperative interactions between CO₂-philic groups in dipeptides significantly enhance CO₂ capture compared to individual amino acids,” said Vogiatzis. “This discovery provides valuable design principles for optimizing CO₂ capture efficiency.”

The study revealed that dipeptides exhibit greater interaction energy diversity than their individual amino acid components, highlighting the critical role of cooperative effects. Statistical analysis showed that asparagine subunits frequently strengthen CO₂ binding, while glycine subunits tend to weaken it.

Beyond fundamental insights, this research lays the groundwork for industrial applications, particularly in direct air capture (DAC) technologies. DAC is a promising technology that pulls CO₂ from air at both concentrated and dispersed locations. By understanding how dipeptides interact with CO₂, researchers can guide the development of next-generation carbon capture materials.

“We believe our findings will contribute to the future design of bioengineered materials for large-scale CO₂ capture. Nature provides incredible solutions, and by mimicking its mechanisms, we can develop transformative technologies to combat climate change,” said Vogiatzis.

This pioneering study exemplifies the power of computational chemistry and bioinspired design in addressing global environmental challenges.

The results of this study were published in the journal ChemPhysChem and highlighted in ChemistryViews.

Rising Scholars: Dylan Andrews

December 6, 2023 by Jennifer Brown

Some students begin their college careers knowing they want a good education but unsure about what comes next, while others move in to their dorms with the next steps toward their career firmly in mind.

Dylan Andrews, senior honors chemistry major, was one of the latter. A native Tennessean, Andrews came to the University of Tennessee, Knoxville in pursuit of an education that would ultimately get him to medical school, starting with an undergraduate degree in chemistry.

“I was fortunate enough to have a really amazing chemistry instructor in high school, Mr. Mark Page. He was one of those teachers who truly makes an impact on you and he really helped me develop a love for chemistry,” said Andrews.

As he pursued his degree at UT, Andrews began to see participating in research as an opportunity to make the most of his time at the university and better prepare himself for the future. He broached the topic with Professor Janice Musfeldt, who was teaching one of his classes at the time.

“I think this is a really good example of how students can get involved in research in the department. Dr. Musfeldt and I built a good relationship over the course of the semester. I also met one of her graduate students and attended a seminar delivered by her colleague, Hans Bechtel. This let me get to know her group and her research, while showing her that I was engaged and interested,” said Andrews.

Hans Bechtel is the infrared program lead for the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory. His ongoing relationship with the Musfeldt Group has led to him co-authoring several publications with its members. Bechtel visited the university to deliver a seminar and, over the course of conversation afterwards, suggested Andrews apply for a Department of Energy (DOE) summer internship at the Lawrence Berkeley Lab later in the year.

The next semester Andrews embraced research in the chemistry department as the next step toward his goals. He registered for the undergraduate research course and joined the Musfeldt lab. Heeding Bechtel’s advice, Andrews also applied for and was awarded a place in the DOE summer program at Lawrence Berkeley.

Near the end of spring semester, Andrews participated in the Department of Chemistry’s annual Undergraduate Research Symposium, presenting a poster to a panel of judges including departmental alumni, retired faculty, and industry partners. This experience gave Andrews his first chance to speak publicly about his research; an opportunity that would pave the way for future poster presentations.

At the end of his internship at Lawrence Berkeley Lab, Andrews entered and placed third in a poster competition designed to evaluate the presentation skills of the participants. The presentations were conducted via Zoom, allowing members of Andrews’ research team in the Musfeldt Group to join and support him.

Andrews plans to graduate in December 2024 and go on to medical school. He believes his experience in the Department of Chemistry and the relationships forged there have prepared him to meet the challenges of a future in medicine.

“Dr. Musfeldt, and really every faculty member I’ve worked with in the department, do everything they can to plug their students into new opportunities and point out things they could do to better themselves as students and researchers. I would probably never have known about that DOE internship if I hadn’t been introduced to Dr. Bechtel,” said Andrews. “The relationships I’ve developed and the support I’ve experienced in the chemistry department have really helped me excel as a student, which will help me through all the next stages of my education and career.”

Vogiatzis Group Publishes in Journal of Physical Chemistry Letters

July 31, 2023 by Jennifer Brown

Grier Jones, fifth year chemistry PhD student, and Associate Professor Konstantinos Vogiatzis recently published a new data-driven quantum chemistry method, based on the reduced-density matrix (RDM) formulation of quantum mechanics, in the Journal of Physical Chemistry Letters. This publication was developed in collaboration with University of Tennessee, Knoxville alumnus Professor A. Eugene DePrince (’05) and his research group at Florida State University. DePrince’s group specializes in the development of novel RDM methods for the treatment of strongly correlated electrons.

Strong electron correlation lies at the heart of molecular quantum mechanics and, in particular, at the heart of electronic structure theory. Configuration interaction (CI) theory provides an exact description of strong correlation, but it suffers from exponential scaling with respect to the number of correlated electrons and orbitals. As an alternative, variational two-electron RDM (v2RDM) methods have been introduced since the energy of a many-electron system can be formulated exactly using the two-electron RDMs (2RDMs). One interesting property is that the 2RDM can be formulated without explicit knowledge of the wave function. In practice, finding a wave function that maps explicitly to the 2RDM can be very tricky, and the resulting deviation between CI- and RDM-based methods can be very large.

To resolve this issue, a collaboration between the Vogiatzis and DePrince groups lead to the development of the data-driven v2RDM (DDv2RDM) method to learn CI-quality energies using data generated using the v2RDM-complete active space self-consistent field (CASSCF) method. Using proof-of-principle calculations, they found that the model learns the correction the v2RDM energy near-chemical accuracy (1 kcal/mol). They also introduced the use of SHapley Additive exPlanation (SHAP) values, a feature importance method based on cooperative game theory, to analyze the how their physics-based features affect model performance. The SHAP analysis confirmed that the features that impact the model performance the most (and least) correspond well to insights based on physical principles.

Read the full article here.

Smith Breaks New Ground with Domain Wall Research

July 25, 2023 by Jennifer Brown

Kevin Smith, recent Ph.D. graduate from the department of chemistry, and Professor of Chemistry Janice Musfeldt have published the results of a collaborative investigation into the properties of ferroelectric domain walls. This research has generated a greater understanding of both a specific material, and domain walls in general, expanding the foundational knowledge critical to effectively using domain walls in future technologies.

Smith joined the chemistry department as a graduate student in 2015 and very quickly began investigating domain walls. Domain walls act as the boundaries between regions, or domains, of materials and have the potential to impact the properties and uses of that material.

Smith’s work specifically investigates the domain walls of ferroelectric materials, which have been a source of interest in the development of electronics. Efforts have been made to use domain walls as functional parts of devices as they could offer high speed memory reading and writing while requiring less energy to function.

Before ferroelectric domain walls can be successfully leveraged, researchers must develop a fundamental understanding of them and how they behave. It has long been hypothesized that these domain walls are atomically thin and conductive, but this had never been confirmed with a direct measurement at the wall. Smith and Musfeldt began investigating ferroelectric domain walls not with the intention of addressing this long-held belief, but with the goal of uncovering foundational information that could contribute to a greater understanding of these materials.

A collaboration with a group of physicists at Rutgers university, led by Henry Rutgers Professor Sang-Wook Cheong, provided Smith the material with which to begin his exploration.

“Our synthetic collaborators at Rutgers grew the material for us and provided some basic mapping on where to look for the domain walls,” said Smith. “We performed a line scan of the material with the near-field infrared microscope at Beamline 2.4 of the Advanced Light Source, or ALS, at Lawrence Berkeley National Lab. That’s when we started seeing these differences that we weren’t expecting to see.”

When thinking of a solid object, the expectation is often that the object is fairly uniform and that the components creating it are evenly distributed throughout that object. However, with the material Smith was investigating, the scan’s results were pointing toward different organizations of the material’s component parts in different regions of the material.

Smith and Musfeldt knew if they were going to uncover the source of these differences, they were going to need to investigate the material further, using the high-resolution infrared technique at the ALS to scan the material more thoroughly.

Beamline 2.4 of the ALS couples an atomic force microscope with synchrotron-generated infrared light to perform nanospectroscopy to examine materials on a much smaller scale than traditional microscopes. The microscope uses extremely sharply focused light delivered to an object at a very close distance. The response of the light as it interacts with the object is then collected and used to determine what is happening in that object.

“Using the ALS allowed us to examine these differences we were seeing in much greater detail. The material that we were studying was grown in such a way that it had two different types of metals in its A-site, scandium and lutetium. The ALS let us tease out three compositional arrangements for these materials that explained the differences. We found regions that were fairly evenly distributed, as well as both scandium-rich and lutetium-rich regions,” said Smith.

In addition to explaining the differences in domains with slightly different local composition, Smith and Musfeldt were able to determine the domain walls themselves were, in fact, much wider than traditionally believed. They also concluded that while they may have different conductivity than the surrounding regions, the domain walls were not metallic.

By successfully imaging ferroelectric domain walls, Smith and Musfeldt have accomplished something that has never been done before. As a result, they not only created a deeper understanding of these domain walls in a specific material, but also upended long-held beliefs about domain walls in general, paving the way for future innovation. Their work further highlights the importance of foundational and exploratory research in the development of future breakthroughs.

“This project really highlights the importance of curiosity in research,” said Musfeldt. “Kevin took an exploratory project and turned it into the most exciting thing in our lab with far-reaching implications.”

New materials are one potential path to improving existing technologies and generating new means of meeting the modern needs of people and society. Materials, however, are only useful insofar as they can be understood. Smith and Musfeldt’s work digs into the fundamental science behind a material’s properties, simultaneously creating a better understanding of that material and creating a roadmap for more effective uses for it in the future.

The full publication describing this research can be read here.

Vogiatzis named Bodossaki Distinguished Young Scientist

June 27, 2023 by Jennifer Brown

Konstantinos Vogiatzis, associate professor in the chemistry department, has been named a Bodossaki Distinguished Young Scientist Award winner. The award recognizes young Greek scientists for their work in a number of academic fields, including science, life sciences, applied science and technology, and the social sciences.

Vogiatzis’ work is centered on the development of computational methods based on electronic structure theory and artificial intelligence. He and his team apply this to chemical systems for clean, green technology.

“As an independent researcher, my work has focused on leveraging machine learning in computational chemistry, using modeling and simulation for the discovery of novel molecules and materials with enhanced properties,” said Vogiatzis. “The guiding objective of my research is to clarify the fundamental physical principles influencing the properties of molecules and materials through the interpretation of experimental data.”

Since 1993, the Bodossaki Foundation has distributed Distinguished Young Scientist Awards every two years. In that time, 57 Greek scientists have been recognized for outstanding research conducted across a global stage. Candidates for the Bodossaki Distinguished Young Scientist Award are nominated by peers, collaborators, and institutions in which they work. Vogiatzis was nominated by Vanda Glezakou, a colleague at Oak Ridge National Laboratory and fellow native of Greece.

Vogiatzis will attend a ceremony in Greece this summer where he will be presented with his award.As a Bodossaki honoree, Vogiatzis joins the ranks of Greek professors working at leading research institutions around the world, including Harvard University, the University of Oxford, and the University of Toronto.

“I would like to express my gratitude to the Bodossaki Foundation, both for recognizing my work and for the honor of being included among the outstanding scientists receiving these awards now and in years past,” said Vogiatzis. “This award is the result of a 17-year course of scientific study that began in the classrooms and research laboratories of Greek universities. This, however, is just the beginning and I look forward to many more years continuing the search for new discoveries in the field of chemistry.”

Vogiatzis joined the University of Tennessee, Knoxville in 2016. Since that time, he has authored more than 40 publications and mentored 15 graduate students. He is the recipient of the 2020 and 2022 Ffrancon Williams Endowed Faculty Award in Chemistry, the 2021 OpenEye Outstanding Junior Faculty Award presented by the American Chemical Society, and a 2021 NSF CAREER award.

Read more about the Bodossaki Foundation and the 2023 Distinguished Young Scientist awardees here.

Vogiatzis Group Publishes in npj Computational Materials

June 23, 2023 by Jennifer Brown

Associate Professor of Chemistry Konstantinos Vogiatzis, in collaboration with Professor of Mathematics Vasileios Maroulas and Eastman Chemical Company, has published a new machine learning model for predicting the properties of new polymeric materials.

Polymers are everywhere. From cookware to medical devices, polymers have become important to modern life due in part to a growing list of potential uses, and desirable properties like high durability and resistance to corrosion.

Creating new polymers can be an expensive, time-consuming process. Because of this, researchers attempt to predict the future properties of polymers using a variety of tools. Computational prediction methods allow researchers to screen polymer combinations for the desired properties before beginning experimentation. However, finding ways to represent polymers as machine-readable inputs can be difficult, creating a challenge for developing accurate prediction models.

Vogiatzis’ team is attempting to tackle these challenges by creating a deep learning method to predict polymer properties called PolymerGNN. PolymerGNN relies on state-of-the-art graph neural networks (GNN) and machine learning to predict the properties of new polymers using a database of complex polyesters.

“Polyesters offer a diverse material space formed by considering many different types of multifunctional acids and glycols, which are the building blocks of these materials,” said Vogiatzis. This, coupled with other complex properties of polyesters, creates a large materials design space Vogiatzis and his team were able to leverage in the development of PolymerGNN.

Vogiatzis worked with Vasileios Maroulas and students Owen Queen, Dr. Gavin McCarver and Sai Thatigotla to develop the general framework and GNN-based machine learning model for PolymerGNN. Collaborators from Eastman Chemical Company synthesized a set of more than 240 polymers and helped compile a database of properties which was used to train PolymerGNN.

Once trained, PolymerGNN accurately predicted both glass transition temperature and intrinsic viscosity. Glass transition temperature is the temperature at which a polymer shifts between a hard state and a softened state. Intrinsic viscosity is a measurement of a polymer’s molecular weight, which can indicate the polymer’s melting point, crystallinity, and tensile strength. These properties are fundamental to the ultimate physical traits of a given polymer and are critical to the development of adhesives, plastics, and more.

Vogiatzis’ team recently published this work in npj Computational Materials, an open access journal from Nature Research. They have also released PolymerGNN as an open-source codebase. Vogiatzis and Maroulas have collaborated on previous machine learning projects published by the American Chemical Society and Nature Communications. Read the most recent publication here.