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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.
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The Impacts of the Molecular Education and Research Consortium in Undergraduate Computational Chemistry on the Careers of Women in Computational Chemistry
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Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.more » « less
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Abstract Students in a second semester general chemistry course used quantum chemical calculations to investigate and reinforce general chemistry concepts. Students explored the isomers of hypochlorous acid, made predictions of miscibility via dipole moments calculated from ab-initio means, experimentally validated/disqualified their miscibility predictions, and used molecular models to visualize intermolecular attraction forces between various compounds. Student responses in pre-/post-exercise assessments show evidence of student learning. Responses in pre-/post-exercise surveys showed an increase in student understanding of basic concepts and of the importance of quantum mechanics in common general chemistry topics.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.jcim.2c00566
