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UT Chemistry Lab Explores Dipeptides for Carbon Dioxide Capture

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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 CO2 in 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.

Vogiatzis Featured Image

Vogiatzis Group Publishes in Journal of Physical Chemistry Letters

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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.

Vogiatzis Group Publishes in npj Computational Materials

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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.

Vogiatzis Publishes in Inorganic Chemistry Frontiers

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The Vogiatzis group recently published a paper in Inorganic Chemistry Frontiers entitled “Data-driven ligand field exploration of Fe(iv)–oxo sites for C–H activation.”

Methane is the main component in natural gas and is expected to become more and more important to the development of fuels and chemicals for applications such as clean energy, light and heat production, and the development of organic chemicals. However, methane’s instability and flammability make storage and transportation difficult. It is possible to improve methane’s stability by converting it into methanol or light hydrocarbons.

One approach to this is the development of new catalysts that mimic naturally existing enzymes. The Vogiatzis group, led by Associate Professor Konstantinos Vogiatzis, focused their research on non-heme Fe(IV)-oxo model complexes.

“Computational studies provide a fundamental understanding of the electronic effects that control the reactivity of the Fe(IV)-oxo species, but also provide directions for the synthesis of the next generation of catalytic complexes and materials,” said Vogiatzis.

Vogiatzis and his team employed machine learning to more quickly and thoroughly investigate possible complexes that may be most effective. They developed machine learning models that use a novel molecular representation based on persistence homology, called persistence images.

“Our methodology uses a novel molecular fingerprinting method based on persistent homology, an applied branch of topology, that can encode the geometric and electronic structure together with molecular topology,” said Vogiatzis. “The new model is trained on accurate data from a few hundred Fe(IV)-oxo complexes and is capable of providing reliable information for thousands of complexes.”

Vogiatzis believes the insights uncovered in this research will aid in the construction of a theoretical framework for the design of novel catalysts for less energetically demanding industrial processes, including the conversion of methane and natural gas. This publication was co-authored by graduate students Grier Jones, Brett Smith, and Justin Kirkland, members of the Vogiatzis research group.

Vogiatzis Group Published in J. Chem. Phys.

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Maria White, Graduate student in Vogiatzis GroupThe Vogiatzis Group published their research “Redox states of dinitrogen coordinated to a molybdenum atom” in The Journal of Chemical Physics. Virginia White, graduate student in the Vogiatzis Group, is the first author on this paper that explores the elucidation of ground and excited states of the MoN2 cluster.

Chemical structures bearing a molybdenum atom have been suggested for the catalytic reduction of N2 at ambient conditions. Previous computational studies on gas-phase MoN and MoN2 species have focused only on neutral structures. Here, an ab initio electronic structure study on the redox states of small clusters composed of nitrogen and molybdenum is presented. The complete-active space self-consistent field method and its extension via second-orderperturbative complement have been applied on [MoN]n and [MoN2]n[MoN2]n species (n = 0, 1±, 2±). Three different coordination modes (end-on, side-on, and linear NMoN) have been considered for the triatomic [MoN2]n[MoN2]n.

“Our results demonstrate that the reduced states of such systems lead to a greater degree of N2 activation, which can be the starting point of different reaction channels.” White said.

Chemistry of Learning: Machines and Humans

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New courses the Department of Chemistry is offering:

Artificial intelligence (AI) rapidly changes many aspects of chemical sciences, from drug discovery, material design, and the discovery of new reactions and molecules till the acceleration of computer sciences and robotics for chemical applications. In Fall 2021, Dr. Vogiatzis will be teaching Machine Learning for Chemical Applications (CHEM420). This course will cover the key aspects of AI and modern chemoinformatics and how they are applied on chemical sciences.
For more information on this course please email kvogiatz@utk.edu.

In the Spring 2022, students may register for Chemistry of the Brain (CHEM340) with Dr. Sharma. This course will be an overview of basic principles of neuroscience with a focus on the function of key neurochemicals and their receptors. Topics include the chemical bases for neuronal membrane transport, electrical excitability, and ion channels; axonal transport; energy metabolism; synaptic transmission; cellular signaling; Ca2+ homeostasis; neurotransmitters; oxidative stress; apoptosis and necrosis; application of neurochemical principles to the molecular bases of neurodegenerative disorders. Co-Requisite: Organic Chemistry. For more information on this course please email bhavya.sharma@utk.edu.

Vogiatzis Group’s Recent Publications

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Research in the Vogiatzis Group centers on the development of computational methods based on electronic structure theory and machine learning algorithms for describing chemical systems relevant to clean, green technologies. They are particularly interested in new methods for non-covalent interactions and bond-breaking reactions of small molecules with transition metals. Their overall objectives are to elucidate the fundamental physical principles underlying the magnetic, catalytic, and sorption properties of polynuclear systems, as well as to assist in the interpretation of experimental data.

Recent work in Coordination Chemistry Reviews “Computational catalysis for metal-organic frameworks: An overview” explores Metal-organic frameworks (MOFs), a family of porous hybrid organic/inorganic materials, have shown great promise for many challenging chemical applications including gas separations, catalysis, and sensors.

“This review highlights recent work performed on catalytic reactions promoted by MOFs from a computational and theoretical standpoint. Computational modeling includes the elucidation of reaction mechanisms, the characterization of electronic structure effects of key intermediates and transition states, and the interpretation of experimental data.” said Gavin McCarver, graduate student.

Vogiatzis also published a paper with his undergraduate advisor, Dimitris Georgiadis. “Professor Georgiadis is the person who taught me first how to do research and follow my scientific goals” said Vogiatzis. Their work  “A Carbodiimide-Mediated P-C Bond-Forming Reaction: Mild Amidoalkylation of P-Nucleophiles by Boc-Aminals” in Organic Letters shares the first example of a carbodiimide-mediated P–C bond-forming reaction. 

The reaction involves activation of β-carboxyethylphosphinic acids and subsequent reaction with Boc-aminals using acid-catalysis. Mechanistic experiments using 31P NMR spectroscopy and DFT calculations support the contribution of unusually reactive cyclic phosphinic/carboxylic mixed anhydrides in a reaction pathway involving ion-pair “swapping”. The utility of this protocol is highlighted by the direct synthesis of Boc-protected phosphinic dipeptides, as precursors to potent Zn-aminopeptidase inhibitors.

Inorganic Chemistry published their work “Electrocatalytic Dechlorination of Dichloromethane in Water Using a Heterogenized Molecular Copper Complex.” 

The remediation of organohalides from water is a challenging process in environment protection and water treatment. They report a molecular copper(I) complex with two triazole units, CuT2, in a heterogeneous aqueous system that is capable of dechlorinating dichloromethane (CH2Cl2) to afford hydrocarbons (methane, ethane, and ethylene). Computational studies provided additional insight into the reaction mechanism and the selectivity toward the CH4 formation. The findings in this study demonstrate that complex CuT2 is an efficient and stable catalyst for the dehalogenation of CH2Cl2 and could potentially be used for the exploration of the removal of halogenated species from aqueous systems.

Vogiatzis Wins OpenEye Outstanding Junior Faculty Award

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Kostas Vogiatzis, assistant professor with the Department of Chemistry, is one of the recipients of the American Chemical Society, Computers in Chemistry Division (ACS COMP) OpenEye Outstanding Junior Faculty Award for Spring 2021.

This competitive and prestigious award identifies junior faculty of promise in the area of computational chemistry and modeling. Vogiatzis will present his research in the upcoming (online) National Meeting of the American Chemical Society. The title of his talk is “Data-driven Computational Chemistry for Noncovalent Interactions of CO2”.

For more information about the award visit https://www.acscomp.org/awards/the-comp-acs-outstanding-junior-faculty-award.

For more information about Dr. Vogiatzis’ research visit https://vogiatzis.utk.edu.

Computational Chemistry and Machine Learning in the Vogiatzis Group

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Research in the Vogiatzis Group centers on the development of computational methods based on electronic structure theory and machine learning algorithms for describing chemical systems relevant to clean, green technologies.

“We are particularly interested in new methods for non-covalent interactions and bond-breaking reactions of small molecules with transition metals,” Vogiatzis said. “Our overall objectives are to elucidate the fundamental physical principles underlying the reactivity and properties of molecules and materials, as well as to assist in the interpretation of experimental data.”

In June 2020, the group was published in Nature Communications for their work “Representation of molecular structures with persistent homology for machine learning applications in chemistry.” This was a unique collaborative opportunity between chemistry department’s Jacob Townsend, graduate student, John Hymel, undergraduate student, Konstantinos Vogiatzis, assistant professor, along with Cassie Micucci and Vasileios Maroulas, Department of Mathematics. The group presents a novel molecular representation method based on persistent homology, an applied branch of topology, which encodes the atomistic structure of molecules.

They began their study by computing with density functional theory (DFT) the CO2 interaction energies of 100 organic molecules. “Since the initial, limited 100 data points were not capturing the diversity of the GDB-9 database, we applied a technique called active learning in order to incrementally obtain data which helped us efficiently screen the 133,885 molecules,” Vogiatzis said. “We found out that the combination of PIs with active learning performed well with data (interaction energies) from only 220 molecules in order to identify new molecules with stronger CO2 binding.”

Their data-driven methodology was able to identify molecular patterns previously unknown to us that increase the CO2 affinity of organic molecules.

The Vogiatzis Group broke a record with their work “Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies.” 

Machine learning methods have enabled the low-cost evaluation of molecular properties such as energy at an unprecedented scale. While many of such applications have focused on molecular input based on geometry, few studies consider representations based on the underlying electronic structure.

Directing the attention to the electronic structure offers a unique challenge that allows for a more detailed representation of the underlying physics and how they affect molecular properties. The target of this work is to efficiently encode a lower-cost correlated wave function derived from MP2 to predict a higher-cost coupled-cluster singles-and-doubles (CCSD) wave function based on correlation-pair energies and the contributing electron promotions (excitations) and integrals.

The new molecular representation explores the short-range behavior of electron correlation and utilizes distinct models that differentiate between two-electron promotions from the same molecular orbital or from two different orbitals. The group presents a re-engineered set of input features that provide an intuitive description of the orbital properties involved in electron correlation. The overall models are found to be highly transferable and size extensive, necessitating very few training instances to approach the chemical accuracy of a broad spectrum of organic molecules.

“Coupled-cluster theory is the level of theory that provides the most accurate quantum chemical results in a reasonable computational time. Typically, we need ~10 minutes for computing the energy of a small molecule with coupled-cluster and for a database with ~133,000 small molecules, we will need ~1,330,000 minutes or ~2.5 years of computations,” Vogiatzis said. “In this work, we demonstrated that we can use the results from only 100 coupled-cluster calculations for training a machine learning model that can predict, without loss of accuracy, the energy of the full 133,000 molecule database a few hours.”

 

 

 

Vogiatzis Group Published in JCTC

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Electronic structure theory describes the motions of electrons in atoms or molecules, and provides a versatile framework for the calculation of molecular geometries, chemical bonding, electronic and spectroscopic properties, reaction barriers, intermolecular interactions, and more. Wave function theory-based methods such as coupled-cluster methods, provide accurate results in a systematic manner, but they typically carry a significant computational cost.

The Vogiatzis group uses machine learning, the field of study allowing computers to learn without explicit programming, to provide novel approaches on the learning of the underlying electronic structure while subverting a significant portion of the computational expense. In a recent article published in the Journal of Chemical Theory and Computation, Jacob Townsend, under the supervision of Kostas Vogiatzis, presents a new efficient methodology that explores the local nature of the correlated motion of electrons which offers scalability and transferability between different chemical systems.

The novel approach provides coupled-cluster quality electronic energies at the cost of second-order perturbation theory (MP2), a computationally more affordable method. As a result, the authors were able to predict energies of a large molecular database, known as the GDB-9 dataset, at high accuracy at Jacob Townsenda fraction of the cost. Additionally, it was shown that the introduced method could be used to accurately predict energies of large chemical systems based on models trained on smaller ones.

Townsend recently received his PhD in the chemistry doctoral program at UT.