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Computational chemistry is no longer seen as just an academic exercise. Researchers in academia and industry are now aware of the benefits associated with theoretical predictions of molecules. However, there is a skills-gap associated with teaching/learning the basics and the applications of computational chemistry. Herein, we describe the development and utilization of several quantum chemical exercises for educational purposes. 

We provide a summary of contemporary computational tools utilized in the study of adsorbate interactions with solid-state materials from the perspective of a quantum chemist. This work contains a focused theoretical primer of interactions between the molecular orbitals of an adsorbate and the electronic bands of a solid as well as a review of the promising methodologies for disentangling these contributions. We apply these tools in a methodological fashion to density functional theory (DFT) calculations of molecular hydrogen (H2), H2 adsorbed to the pristine Cu(111) surface, and H2 adsorbed to a single atom alloy comprised of palladium and copper (Pd/Cu(111)) to provide chemically intuitive explanations of bonding in these systems. 

We explore the charge transfer events four different classes of N-heterocyclic carbenes (NHCs) with a Pd/Cu(111) single atom alloy surface. We provide a frontier molecular orbital approach to understand similarities and differences between these systems. We demonstrate that this approach can be applied to the PDOS, COHP, and molecular charges to better understand the surface-adsorbate system. Density of This work emphasizes the composition- and geometry-dependent nature of NHC adsorption on SAAs and provides insights into tailoring these systems for catalytic and storage applications. 

Molecular corking is a phenomenon where a molecule selectively binds to the catalytic atom of a single-atom alloy (SAA) and prevents the recombination of atomized gas molecules at the surface of a metal, functionally trapping them. A necessary feature of a molecular cork is strong, yet reversible binding and thermal stability. There is active experimental and theoretical research on potential candidates, which rank the viability of molecular corks based on their ability to donate electrons into sigma bonds and accept back-donation from the metal via pi bonding. This is often assessed experimentally and computationally using chemical shifts in NMR of selenium and phosphorus adducts as well as other metal complexes. A prime candidate for this purpose are N-heterocyclic carbenes (NHC). However, there have been few studies that perform bond decomposition analysis of periodic quantum mechanical calculations to qualify if the observed chemical shifts in NMR and binding strengths on metals are in fact due to sigma donation and pi back bonding. Moreover, the role of conformational flexibility plays in reported NMR chemical shifts is relatively unexplored. Using periodic vdW-DFT calculations and localized bonding MOs extracted from periodic plane-wave basis sets, along with molecular calculations, we seek to answer these questions for simple, but representative molecular systems bound to Cu-Pd SAA surfaces. 

This study investigates the integration of generative artificial intelligence (Gen AI) into a General Chemistry II laboratory at St. Bonaventure University to assess its impact on student learning, inquiry, and chemical reasoning. In a redesigned of a common thermodynamics/equilibrium experiment involving cobalt(II) chloride, students used ChatGPT and a retrieval-augmented Gen AI (BonnieChemBot) to assist in calculating equilibrium constants, thermodynamic values, and answering open-ended chemical questions. The lab targeted four key educational goals: (A) mastery of chemical concepts, (B) development of prompt engineering strategies, (C) facilitation of inquiry-driven dialogue with instructors, and (D) critical evaluation of Gen AI outputs by students using chemical intuition. Students attempted all analyses twice: first independently, then with the assistance of Gen AI. Finally, they were asked to reflect on their trust in the Gen AI solutions and generate final answers. Instructor intervention in post-lab analyses was limited to cases where students had clearly attempted the first two steps in this process. While students were encouraged to practice prompt engineering, no grades were assigned to its execution. Instead, grading focused solely on students’ final answers. 

We apply non-local density functional theory calculations to determine the impact of backbones and functionalization of different N-heterocyclic carbenes (NHCs) adsorbed to different single atom alloys (SAAs). A frontier molecular orbital approach is applied to these systems to understand the chemistry occurring. The Local Orbital Basis Suite Towards Electronic-Structure Reconstruction (LOBSTER) program was utilized to project an atomic orbital (AO) basis from our PAW simulations to allow for a MO-oriented bonding analysis MOs of the adsorbate molecules were extracted from our planewave VASP calculations utilizing the linear combination of fragment orbitals (LCFO) method implemented in LOBSTER. 

N-Heterocyclic carbenes (NHCs) are an interesting family of molecules that have potential applications in hydrogen storage via the “molecular corking effect”. Benefits of using NHC backbones as molecular corks include their modularity via functionalization and synthetic diversity. Changing the functional groups can lead to new properties depending on their donation or withdrawal of electron density. Additionally, mono- and di-protonation of the carbene on the various backbones was observed to have significant effects on their proton affinity. We hypothesize that proton affinity can be utilized to understand the sigma donation effects of the evaluated molecules, which are expected to be predictive of the bond strength of an NHC to a surface. 

We discuss several different computational chemistry exercises that are appropriate at the undergraduate level. We highlight how computational chemistry can be integrated into the undergraduate chemistry curriculum with topics ranging from carbon nanotubes, catenanes, paramagnetic NMR, resonance, and other topics. 

An in-silico exercise was developed for a general chemistry laboratory course at St. Bonaventure University in which students examined potential energy surfaces, molecular orbital diagrams, and how bond orders and Lewis structures are connected. Pre- and post-assessment data suggests that, though students learned from the exercise, they are not connecting the concepts of bond order, Lewis structures, and resonance. There was a statistically significant improvement in the assessment scores before and after the laboratory experiment, and there was no statistical difference between the post-assessment and the follow-up assessment, which occurred after students completed the lab report 1 week after the initial experiment. The data suggest an improved understanding of computational chemistry concepts as well as improvement in the individual concepts of resonance, Lewis structures, and bond orders. However, an assessment question connecting these concepts did not show an improvement. An additional questionnaire was conducted to explore this discrepancy. This study indicates that more investigation is necessary with regard to students’ ability to make logical connections among bond orders, Lewis structures, and resonance.