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Dear VIPEr friends,
I am teaching an advanced undergraduate group theory, molecular orbital theory, computational chemistry course this semester and having a ball. My question has to do with what we expect students to learn (what will they know and be able to do) when doing computational chemistry. I'm looking for answers beyond the obvious: learning the mechanics of setting up and visualizing the results of a calculation.
My course emphasizes molecular orbital theory, so the students are able to use group theory to qualitatively develop a molecular orbital diagram for a small molecule. What chemical insight or knowledge does CALCULATING the molecular orbitals add?
Writing good learning goals is hard. Help!
I think that one goal that is that students would be familiar with the different levels of calculation, their strengths and weaknesses, in order to evaluate calculations they see in the literature. Students should be able to appreciate what an "expensive" calculation is, but why we are willing to spend time in order to "buy" a more accurate result. This may fall under your "mechanics," but I think this is a valuable lesson. I took a similar course as an undergraduate, and for the final exam, we were given a certain number of problems within the exam time. We could choose the level of theory and basis set required to find the best answers, but it was impractical just to do everything at the highest level of theory because we would run out of time.
Additionally, students should understand that much of our understanding of how chemical bonding (and thus, chemistry) works is based on models, from Lewis dot structures to density functional theory. (This is the premise for your course.) For all of these models, we use mathematics to describe the system. If we understand the system and can account for or approximate the important forces, the calculations and our experiments will come up with similar answers. However, if they come up with different answers, this can help us see that the interpretation of our experiments may be incorrect. Computational chemistry demonstrates for us when our models are not right by giving us a number to compare to experimental observables.
Additionally, calculations help us deal with larger, more complex systems than group theory and chemical intuition can handle.
So these aren't written as learning goals, but maybe this helps open the discussion?
Thanks! This is helpful. I would have been terrified by your undergrad test, because I would have been paralyzed trying to make a choice under time pressure. But it certainly makes the point!
I like your thinking about "models" and the idea of using calculations as a check. I wonder if some computational chemists would take offense at that? It seems to imply a "clean up" view of computational chemistry instead of a "pushing back the frontiers of science" view. But maybe it's really more of a "walking side-by-side" perspective. Hmm.
One person told me "Computational chemistry allows you to be quantitative," and I thought "Good, and that means?" But I can see, of course, that numbers allow you to compare to experiment. I would hope that this might then inspire you to ask new and useful questions. Now quite sure how I would assess that as a possible learning outcome.
Thanks again.
One idea I've been playing with in the past couple of years is a student computational lab evaluating Koopman's theorem to estimate the Gibbs free energy and Cell voltage of a particular redox reaction that can then be compared to experimental data. It was one of several possible exercises that students could have done, but I think it was the most interesting of the ideas I came up with.
Once I have picked out a set of compounds to calculate that gives reliable data with the fewest computational snags, I'll post the exercies as a learning object.
The general outcome of the lab is that Koopman's theorem doesn't work all that well for metal compounds because they are often open-shell molecules, but it can open up a discussion of what a redox-active molecular orbital (RAMO) is.