Vogiatzis Lab Published in Nature Communications

June 29, 2020 by Kayla Benson

Machine learning applications for chemical problems have been rapidly increasing. Their popularity is justified since they have led to the discovery of new molecules and materials with enhanced properties, new reactions, or have contributed to the reduction of computational effort needed of complex calculations and simulations.

The Vogiatzis Lab seeks to address how a computational algorithm can efficiently “read” and “learn” patterns from molecular structures in their research “Representation of molecular structures with persistent homology for machine learning applications in chemistry” recently published in Nature Communications.

In this collaborative work between Jacob Townsend, John Hymel and Konstantinos Vogiatzis (Chemistry, University of Tennessee) and Cassie Micucci and Vasileios Maroulas (Mathematics, University of Tennessee), the group is presenting a novel molecular representation method based on persistent homology, an applied branch of topology, which encodes the atomistic structure of molecules.

A molecule is mapped into a persistence diagram, a two-dimensional point summary, which demystifies the connected components and the empty space that exist in a molecule based on the atom types and the distances among them. A persistence diagram is further vectorized to a persistence image (PI), a weighted representation of the diagram, which captures the chemically driven uncertainty. The PI in that sense is a “molecular fingerprint”, and when used with machine learning, offers an efficient and reliable approach to screen large molecular databases when compared to other popular molecular representation schemes.

The efficiency arises from the low computation effort needed to compare a large number of fingerprints, and the similar-size representations that are generated, independently of the molecular sizes.

The group demonstrates the applicability of the PI method by screening a large molecular database (GDB-9) with 133,885 organic molecules. Their target was to identify novel molecular units that selectively interact with CO₂ and can be used as building blocks of materials, such as polymeric membranes.

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.

 

Awards Within the Vogiatzis Group

January 5, 2020 by Kayla Benson

The Vogiatzis Group’s research 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. Our 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 group awards:

1. 1. Mrs. Alexa Griffith, an undergraduate student pursuing research in my group, was awarded a DAAD exchange fellowship for pursuing research for three months at the Technical University of Kaiserslautern, Germany (2018).
   2. Mr. John Hymel, an undergraduate student pursuing research in the Vogiatzis Group, won the Award of Excellence in Natural Sciences and Office of Research and Engagement Bronze Award at the Exhibition of Undergraduate Research and Creative Achievement (EUReCA) (2018).
   3. Mr. Jacob Townsend, graduate student in the Vogiatzis Group, won the best Lightning Talk Award at PsiCon, the annual Psi4 software developers meeting (2018).
   4. Mr. Grier Jones, graduate student the Vogiatzis Group, received a travel grant from the Molecular Science Software Institute (MolSSI) to attend the MolSSI Workshop: The Open Molecular Science Cloud in Perugia and Rome, Italy (2019).
   5. Ms. Rebekah Duke, REU student who worked in the Vogiatzis Group during Summer 2019, was accepted to present her results that obtained at the University of Tennessee at the Posters on the Hill, an annual undergraduate poster session on Capitol Hill, Washington, DC (2020).

Vogiatzis’s Hot Paper

October 2, 2019 by Kayla Benson

Metal-organic frameworks (MOFs) are a class of hybrid inorganic/organic materials with exceptional properties that have been used in many chemical applications such as catalysis, gas separations, and sensors.

For the first time, gas‐phase catalytic activity occurring at the metal nodes of a crystalline MOF was reported in a recent collaborative work among the groups of Professor Kostas Vogiatzis (theory), and Professors Donna Chen (catalysis, characterization) and Natalia Shustova (synthesis) from the University of South Carolina. 

Vogiatzis said, “Also, this study shows for the first time a rhodium at oxidation state II can be introduced in a MOF. “

The electronic structure of the heterobimetallic Cu-Rh node and the reaction mechanism of the hydrogenation of propylene was elucidated by post-doctoral associate Rajesh Thayalan in Professor Vogiatzis research group.

The conclusions of this collaborative work were published in Angewandte Chemie International Edition in an article that was selected as “hot paper” by the editor.

 

Vogiatzis Group Published in the Journal of Physical Chemistry Letters

July 15, 2019 by Kayla Benson

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.

One of the targets of research in Vogiatzis’s group is to accelerate electronic structure theory calculations using machine-learning, which is the field of study that allows computers to learn connections in data without explicit programming.

Machine learning has changed our lives through improved speech recognition, automated vehicle operation, optimized web searching and recommendation, and beyond. Graduate student Jacob Townsend mentions, “Our goal is to take this technology, and allow our calculations to learn from previously executed calculations without introducing any approximations or alchemical approaches. Therefore, the desirable accuracy is reached with significantly less computational effort.”

In their recent publication entitled “Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver” published in the Journal of Physical Chemistry Letters, the team introduces a novel strategy to accomplish this speedup in a goal to change the way we will execute calculations in the future.

Vogiatzis Group Lands Second Cover of J. Phys. Chem. A

April 25, 2019 by Kayla Benson

The Vogiatzis Group published their research “Understanding the Nature of Weak Interactions between Functionalized Boranes and N₂/O₂, Promising Functional Groups for Gas Separations” in The Journal of Physical Chemistry A. This work also landed the second cover for the April 18, 2019 issue. 

This research explores the separation of nitrogen and oxygen gases, which is considered as a very challenging process, since both O₂ and N₂ are nonpolar molecules with similar kinetic diameters.

Electronic structure theory can provide a fundamental understanding of effects that can lead to selective binding of nitrogen or oxygen gas for the development of novel separation processes. Boranes can bind dinitrogen through a dative bond, where the boron acts as a σ acceptor and back-donates through π orbitals.

To better understand these interactions, the group has performed highly accurate CCSD(F12)(T) and CCSDT(Q) computations for the BH₃–N₂ and BH₃–O₂ complexes. The coupled-cluster binding energies were used as reference for benchmarking different density functionals, and larger functionalized boranes were examined at the M05/def2-TZVPPD level. Symmetry adapted perturbation theory (SAPT) calculations were performed for the elucidation of the nature of the interaction between nitrogen and substituted boranes and how direct or distal functionalizations affect the strength of the weak dative bonds. By use of these methods, several boranes were found to bind N₂ over O₂.

These molecular species are promising functional groups for incorporation into the next generation of advanced materials for efficient N₂/O₂ separations.

Vogiatzis Group Published in Chemical Reviews

March 1, 2019 by Kayla Benson

The Vogiatzis Group published their work “Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities” in Chemical Reviews.

Their work discusses the challenges and capabilities of modern electronic structure methods for studying the reaction mechanisms promoted by 3d transition metal molecular catalysts. Particular focus is placed on the ways of addressing the multiconfigurational problem in electronic structure calculations and the role of expert bias in the practical utilization of the available methods.

The development of density functionals designed to address transition metals is also discussed. Special emphasis is placed on the methods that account for solvation effects and the multicomponent nature of practical catalytic systems. This is followed by an overview of recent computational studies addressing the mechanistic complexity of catalytic processes by molecular catalysts based on 3d metals.

Conventionally, computational studies on catalytic mechanisms are heavily dependent on the chemical intuition and expert input of the researcher. Recent developments in advanced automated methods for reaction path analysis hold promise for eliminating such human-bias from computational catalysis studies.