Today is a great day for computational chemistry. It is good to see the detailed Nobel announcement acknowledge other stalwarts of the field like Peter Kollman (who created AMBER, a package similar to CHARMm by Karplus et al). I have no doubt that Kollman would have received this prize had he been alive today.
Chemistry is applied physics. Chemistry answers that H2 and O2 can be joined to form water under certain conditions, but doesn't answer the "Whys"
Physics answers that. And the answer goes around orbitals, energy states, electrons, etc.
Chemistry does answer the whys beyond product yields, why certain elements react is vastly covered throughout organic, inorganic & physical chemistry . It is the basis of these sub-fields, and how this is determined is looking at orbitals, energy states etc...
So, Pauli's exclusion principle is Physics or Chemistry? I'd say it's "both".
I never said reductionism is a one way street.
But in simulations you usually work bottom-up (in abstractions). Of course, sometimes you can't do that, because it's overly complex.
My field (electrical engineering) has a lot of physics as well and I don't mind if those who invented the techniques I use were physicists or engineers (or something else), or if such and such things are in the "engineering" domain or the "physics" domain, I just use them. The point is moot.
You know the main mechanisms at work, but that does not mean you can study all the processes. Sometimes it's just that the sheer size of the mathematical model itself is basically impossible to study without a computer. Oftentimes, though, it's the inadequate formulation of a model that doesn't allow you to simulate it.
A good simulation is an immensely useful tool. There are phenomenons which you know and can simulate, but you cannot easily measure their parameters while they are happening (e.g. you risk disrupting the phenomenon because of your measurement installation). I imagine this is even more so for chemists.
Real-life example from my own research: we worked on tools that helped engineers who designed really high-frequency ICs (think tens of GHz) study things like cross-talk through the substrate. The mechanism itself is basically well-understood, but save for really, really simple structures that are nothing like those in an IC, you can't solve that by hand. Of course, ours were modest achievements, but the point is that this kind of research can open new gates and shouldn't be considered "second-rate".
Bonus points if they observe real world reactions that agree with the model after the simulation has run.
1. You take a lot of empirical observations of known reactions, build a predictive model, then apply it to contexts you don't have empirical observations of.
2. You program in the low level rules of chemistry (e.g. quantum mechanics), and see how the scenario plays out at a higher level (molecular reactions).
I think in their work they are more number 1. A predictive model of proteins without having the simulate the (exceedingly expensive) low level details of QM
TLDR: Approximations are everywhere, make sure you work that into your conclusions.
Also software in chemistry can be used to simulate reactions and processes, which can take years to analyze with conventional methods(if at all possible), like folding protein chains.
http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo
Similarly, David Baker's lab has focused on producing the folded conformations of proteins in hours to days using simplified models that are designed for that task: