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January 2021

Archives for January 2021

Musfeldt Group Published in Nano Letters

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The Musfeldt Group published their work “Excitations of Intercalated Metal Monolayers in Transition Metal Dichalcogenides” in Nano Letters.

They combine Raman scattering spectroscopy and lattice dynamics calculations to reveal the fundamental excitations of the intercalated metal monolayers in the FexTaS2 (x = 1/4, 1/3) family of materials. Both in- and out-of-plane modes are identified, each of which has trends that depend upon the metal–metal distance, the size of the van der Waals gap, and the metal-to-chalcogenide slab mass ratio.

They test these trends against the response of similar systems, including Cr-intercalated NbS2 and RbFe(SO4)2, and demonstrate that the metal monolayer excitations are both coherent and tunable.

They discuss the consequences of intercalated metal monolayer excitations for material properties and developing applications.

Bailey Lab Published in ChemBioChem

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The Bailey Lab also published “Site Directed Mutagenesis of Modular Polketide Synthase Ketoreductase Domains for Altered Stereochemical Control” in ChemBioChem.

Bacterial modular type I polyketide synthases (PKSs) are complex multidomain assembly line proteins that produce a range of pharmaceutically relevant molecules with a high degree of stereochemical control. Due to their colinear properties, they have been considerable targets for rational biosynthetic pathway engineering. Among the domains harbored within these complex assembly lines, ketoreductase (KR) domains have been extensively studied with the goal of altering their stereoselectivity by site-directed mutagenesis, as they confer much of the stereochemical complexity present in pharmaceutically active reduced polyketide scaffolds. Here we review all efforts to date to perform site-directed mutagenesis on PKS KRs, most of which have been done in the context of excised KR domains on model diffusible substrates such as beta-keto N-acetyl cysteamine thioesters. We also discuss the challenges around translating the findings of these studies to alter stereocontrol in the context of a complex multidomain enzymatic assembly line.

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.”

 

 

 

Larese Group Featured Cover on J. Chem. Phys. C

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The Larese Group’s research Adsorption of Pentane and Hexane Thin Films on the Surface of Graphite(0001) was featured on the cover of The Journal of Physical Chemistry C.

Surface adsorption plays an important role in a variety of industrial and technological processes, especially in energy conversion, storage, and transformation. As a result, there is a growing need for advancing the current understanding of fundamental interactions that govern these types of processes. 

This research characterizes the interaction of n-pentane and n-hexane with graphite using high-resolution volumetric adsorption isotherms along with molecular dynamic simulations. The thermodynamics of adsorption were obtained for n-pentane and n-hexane adsorbed on the basal plane of graphite in the temperature ranges 190–235 and 230–280 K, respectively, using high-resolution volumetric adsorption isotherms. These linear molecules exhibit a van der Waals interaction with the surface of graphite(0001) and yield an overall greater binding than on boron nitride and MgO(100).

The averaged areas per molecule calculated for the fluid monolayer phases were determined to be 52.03 and 61.35 Å2 for pentane and hexane, respectively, which are in agreement with previous diffraction measurements performed for the monolayer solid. MD simulations were performed in order to provide additional microscopic insight.

Density profiles normal to the graphite substrate revealed that the stabilization of the layer nearest to the surface by the fluid multilayer exists for linear alkanes as small as pentane, however to a much lesser extent than that observed previously for adsorption on boron nitride and MgO. Intermolecular radial distribution functions and diffusion coefficients derived from the molecular trajectories suggest that a liquid crystal phase exists in the layer nearest the surface at temperatures well above the bulk triple-point temperatures.