I love computational research more than most, seeing as I'm a scientist and a coder. However, I feel the need to weigh in with a counterpoint on this. I've sp

I love computational research more than most, seeing as I'm a scientist and a coder. However, I feel the need to weigh in with a counterpoint on this. I've spent the last eight years measuring a system that was well described by both theory and computational experiments. It was exactly one of those systems where the results could not be measured experimentally and simulation was the only path forward. We then built an instrument which could measure the results experimentally. Since this system was so well described by simulation and theory, we used it as a calibration sample before doing real science. Except the results didn't match the computations at all. I spent years tracking down problems in the instrument (some of which were real) before finally setting in on the fact that the theory and calculations were simply wrong. Just as you're always experimentally limited in what you can measure, you're resource constrained in what you can calculate. Sometimes, you need to drop down another physics abstraction level before you can get the right answer. However, experiments don't care about your abstraction level and get it right every time. Performing physical experiments is a waste of time and money if you can just simulate the results. However, you don't know if you can simulate it until you've performed the experiment. Every scientist I've known has, at least once, run a simulation and later found that it wasn't even close to the experimental results. Sometimes it's a leaky abstraction in the simulation (e.g. Ignoring the anti-reflective coating on a mirror). Sometimes it's a leaky abstraction in the experiment (e.g. ignoring the pH differences between H2O and D2O). Sometimes it's new physics, but not very often. Sometimes it's exactly like you predicted. Until you leave the keyboard and enter the lab, you won't know.