Disclaimer: I'm only a freshman, so there's still a ton I don't know :)

Right now the lab is having me get comfortable using software like Gaussian and ORCA by simulating a bifurcating reaction. This is a reaction that, depending on the catalyst's momentum, will change what site it bonds to (it makes either a 6-membered or 7-membered ring). I'm finding the intermediate states (where the molecule is most stable) and transition states (the tipping point), and then running trajectories to see which output is more likely.

Once I've finished simulating that, I should be comfortable enough with the process to jump on the bigger project, which is machine learning interatomic potential (MLIP) model distillation. There's a lot of exciting work around speeding up DFT methods by using machine learning (note this is not generative AI, it's merely predicting the molecule energy based on atomic positions). So my one year goal is to get on that project and start contributing.

My five year goal is to, well, graduate. But then I'll probably do a PhD in computational chemistry, since I'm really interested in ways to speed up and scale existing methods. My big dream is to simulate large biological systems while still having bond formation and breaking, to automatically elucidate biochemical pathways, but there's still a lot of steps in-between.