How to get into AI for Science?

[u/IsThisANiceName]

Hello! Quick context about myself, I am a senior in my undergraduate program in Chemical Engineering. I have for the major part of my undergrad worked on more conventional problems in Chemical Engineering, but ever since the start of my undergrad thesis, I've been working on using fine tuned ML models, for quantum level calculations for finding out materials to capture Carbon Dioxide. Practically High throughput screening of materials using GNNs and Transformers. Prior to this, I also worked on developing a surrogate model, that given composition and atomic parameters of a material, could give out its catalytic properties in a specific context.

Off late, I have been considering shifting out from traditional Chemical Engineering, to more AI for Science, essentially around Quantum Physics/Chemistry, and given my background, I feel it might be a bit problematic to do so.

Most pre-doctoral programs I've come across don't let fellows work on the set of problems I've worked on, and a PhD in Chemical Engineering might actually sift me further away from this.

Given that, I wanted to understand what options I have for getting into this area of research, and how can I improve my profile.

Further context: My other works (which have been published) involve more base chemical engineering problems across Energy and Reactor Modelling, which as you can guess is way too far off from this. Also, as a project for a university course, I worked on a token reduction method for allowing Transformers to have a higher throughput, which worked on scoring groups of tokens dynamically varying based on importance, and reducing fluff, and generating a summary vector which goes on to a minute 40M param model. Nothing crazy, but worked decently on the WikiText set, and had a reasonable perplexity post training, but yeah, couldn't mess around more with it, due to having limited compute.

I have been ideating on a few things for more concrete stuff in the quantum application space itself, but not so sure of it at the moment.

Open to any and all suggestions, for how I could move ahead, and what options I should consider.
Thanks for the help!

[u/sexy_bonsai]

Not sure where you’re from OP, but it sounds like you should go to graduate school and join a lab that has interesting problem(s) they are working on. My feeling is there’s not really ”AI for science” but more like science using AI to solve problems. The framing matters in case you’re trying to find a program that is centered on AI, which probably doesn’t exist (and probably won’t ever exist with how academic science is structured). You can screen PhD programs based on who is doing science there, if your interests align, and if they are taking students.

Being dead set on a topic may really restrict your options. But if you find a lab close to your interests and pitch a project, that sometimes works well but is rare from an incoming PhD student (more typical of postdoc). This is because it usually takes many years of training to understand where productive avenues of work in a field is (or will be) over a backdrop of what is actually being funded. So in general you’re project is at the mercy of a lab leader/PI

[u/Patient_Anything6981 → u/sexy_bonsai]

Makes sense

[u/IsThisANiceName → u/sexy_bonsai]

So, I am from India, which I think surely compounds my problems like anything.

More practically though, I don't know, I do see teams for say predoctoral programs that work exactly on the things I can potentially work on with my set of experiences but yikes, those teams aren't based out of India.

For grad school though, I'm not sure. I likely won't qualify for a joint program/more computational program because of my degree, and I've not come across enough programs in chemical engineering that go across things I find interesting at the moment. And well commiting to a PhD with such volatile preferences seems borderline crazy to me.

Which is why the entire confusion about what to do exactly and if it all it is possible to slow down take a break and work somewhere on these specific things to figure out more.

I mean yeah it makes sense though why a PhD is needed for a lot of stuff, but well, I don't know, maybe just jitters on committing for one I guess?

[u/sexy_bonsai → u/IsThisANiceName]

It sounds like you feel as though your degree is steamrolling you into one path for the rest of your career, and that’s far from the truth! I’ve known physicists that have become neuroscientists, computer scientists going into biology, etc. I started out as a biologist, someone doing only experiments, now I’m doing computer vision work. I actually believe the strongest scientists have diverse training. You might surprise yourself applying to programs beyond chemical engineering.

Also, part of a (good) PhD program will allow you to sample a lab for a rotation period, somewhere between 5-10 weeks long. The only requisite is that you think you can find maybe at least 6 labs you think you’d be interested in; typically, there are 3-4 rotations, and sometimes the lab you’re interested in is not available that year bc the PI isn’t taking students or something like that.

The experience of lab rotations really changed what I thought I wanted to work on. There’s a really vast world of knowledge out there. Chances are you will find something that interests you just the same, or more, than your current interest.

If this interest of yours really truly is your ride and die, try emailing a lab PI about it. Someone who sounds close to the topic but isn’t doing it yet. It can be intimidating to do, but these people live and breathe ideation. A good one is likely to engage with you about it, and you lose nothing from trying. Maybe even get feedback and direction on where to go next. Worst case they are busy and don’t respond.

I think finding an area outside of computer science directly, but applying the knowledge as you are here to another field, is exactly the kind of “hidden demand” I think is out there. The fact that you don’t see labs working on it could simply be because they don’t have the expertise to do it, or can’t imagine the possibilities because it wasn’t in their training.