[smj-edison]

Depends what level of accuracy you want. I just started in a computational chemistry lab so I'll probably get some details wrong, but for small systems, you can use a method called CCSD(T) for up to ~20 atoms, but it scales O(N^7). I've been mainly using DFT for the systems I've been simulating, which scales O(N^3). I've been running a system with about 50 atoms with a decent basis set (how the orbitals are modelled), and it takes about 30 minutes for each optimization step with 24 cores and 48 GB of RAM.

DFT works in many cases, but in some cases it doesn't estimate the energy right, due to how it bypasses some correlation calculations. Bonds are extremely sensitive to energy calculations, so you need to get super close to the actual energy in order to get useful results.

Anyways, someone with more experience here could probably add more, but that's what I've picked up so far.

[vessenes]

Cool details, thanks. To help me understand your life, what would be like a one year and a five year research goal for you? I never spent time in lab sciences so it’s kind of a black box for me.

[smj-edison]

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.

[WillAdams]

Good luck!

I assume you are familiar with:

https://matt.might.net/articles/phd-school-in-pictures/

I hope and pray that your research helps to make the world a better place and that the rest of us can use your knowledge to help to make the world a place which merits your research.

[smj-edison]

Thank you for the kind words! I've been wanting to do this research precisely because of firsthand experience with how hard chronic illness can be, and I'm hoping to attack it with a systems approach.

I haven't seen that website before, but it sounds pretty accurate from what I've heard. It's insane how high of a mountain needs to be climbed just to catch up to the state-of-the-art, and how much work is needed to push through to figure out something truly new.

Here's to making the world a better place!

[WillAdams]

I view the difficulty/breadth and depth of knowledge necessary to begin a PhD as a tribute to humanity's successes (and hope to embark upon my own after retirement, though it'll be in CNC machining).

[not-a-llm]

do you think quantum computers would help simulating this? I've seen contradictory opinions from the experts - it can in theory but not really in practice (even assuming sufficiently large quantum computers will be built)

[smj-edison]

I'm only a freshman, so I don't feel very qualified to comment on that :) I hope so though!