[dmarchand90]

To plug my own field a bit, in material science and chemistry there is a lot of excitement in using machine learning to get better simulations of atomic behavior. This can open up exciting areas in drug and alloy design, maybe find new CO2 capturing material's or better cladding for fusion reactors, to name just a few.

The idea is that to solve these problems you need to solve the schrodinger equation (1). But the schrodinger equation scales really badly with the number of electrons and can't get computed directly for more than a few sample cases. Even Density Functional Theory (DFT), the most popular approximation that still is reasonably accurate scales N^3 with the number of electrons, with a pretty big pre factor. A reasonable rule of thumb would be 12 hours on 12 nodes (each node being 160 cpu cores) for 256 atoms. You can play with settings and increase your budget to maybe get 2000 (and only for a few timesteps) but good luck beyond that.

Machine learning seems to be really useful here. In my own work on aluminium alloys I was able to get the same simulations that would have needed hours on the supercomputer to run in seconds on a laptop. Or, do simulations with tens of thousands of atoms for long periods of time on the supercomputer. The most famous application is probably alphafold from deep mind.

There are a lot of interesting questions people are still working on:

What are the best input features? We don't have any nice equivalent to CNNs that are universally applicable, though some have tried 3d convnets. One of the best methods right now involves taking spherical harmonic based approximates of the local environment in some complex way I've never fully understood, but is closer to the underlying physics.

Can we put physics into these models? Almost all these models fail in dumb ways sometimes. For example if I begin to squish two atoms together they should eventually repel each other and that repulsion force should scale really fast (ok maybe they fuse into a black hole or something but we're not dealing with that kind of esoteric physics here). But, all machine learning potentials will by default fail to learn this and will only learn the repulsion to the closest distance of any two atoms in their training set. Beyond that and the guess wildly. Some people are able to put this physics into the model directly but I don't think we have it totally solved yet.

How do we know which atomic environments to simulate? These models can really only interpolate they can't extrapolate. But while I can get an intuition of interpolation in low dimensions once your training set consists of many features over many atoms in 3d space this becomes a high dimensional problem. In my own experience, I can get really good energies for shearing behavior of strengthening precipitates in aluminum without directly putting the structures in. But was this extrapolated or interpolated from the other structures. Not always clear.

(1) sometimes also the relativistic Dirac equation. E.g. fast moving moving atoms in some of the heavier elements move at relativistic speeds.

[rsfern]

More physical ML force fields is a super interesting topic that I feel like blurs the line between ML and actually just doing physics. My favorite topic lately is parametrizing tight binding models with neural nets, which hopefully would lead to more transferable potentials, but also let you predict electronic properties directly since you’re explicitly modeling the valence electrons

Context for the non-mat-sci crowd - numerically solving Schrodinger essentially means constructing a large matrix that describes all the electron interactions and computing its eigenvalues (iterated to convergence because the electron interactions are interdependent on the solutions). Density functional theory (for solids) uses a Fourier expansion for each electron (these are the one-electron wave functions), so the complexity of each eigensolve is cubic in the number of valence electrons times the number of Fourier components

The tight binding approximation is cool because it uses a small spherical harmonic basis set to represent the wavefunctions in real space - you still have the cubic complexity of the eigensolve, and you can model detailed electronic behavior, but the interaction matrix you’re building is much smaller.

Back to the ML variant: it’s a hard problem because ultimately you’re trying to predict a matrix that has the same eigenvalues as your training data, but there are tons of degeneracies that lead to loads of unphysical local minima (in my experience anyway, this is where I got stuck with it). The papers I’ve seen deal with it by basically only modeling deviations from an existing tight binding model, which in my opinion only kind of moves to problem upstream

[occamschainsaw]

I am currently working on physics-informed ML models for accelerating DFT calculations and am broadly interested in ML PDE solvers. Overall, I think physics-informed ML (not just PINNs) will be very impactful for computationally heavy science and engineering simulations. Nvidia and Ansys already have "AI" acceleration for their sims.

https://developer.nvidia.com/modulus

https://www.ansys.com/ai

[telotortium]

I was a grad student in an ab initio quantum chemistry group about a decade and a half ago. I was working on using DFT with correction from various post-Hartree-Fock methods for long-range correlation - it worked okay, but it was clear that it would never scale up to large non-crystalline molecules. DFT did somewhat better on solid-state systems. The scaling issue really killed my motivation to work on the field, and led me to taking a master's degree and leaving early. So it's been fascinating to hear about deep learning approaches to computational chemistry recently - almost like the revenge of the molecular mechanics models, which our group disdained a little but was also by far the most-used feature of the software package for which we wrote our codes.

[mynameismon]

> In my own work on aluminium alloys I was able to get the same simulations that would have needed hours on the supercomputer to run in seconds on a laptop.

Could you elaborate on this further? How exactly were the simulations sped up? From what I could understand, were the ML models able to effectively approximate the Schrodinger's equation for larger systems?

[dmarchand90]

What you do is you compute a lot of simulations with the expensive method. Then you train using neural neural networks (well any regression method you like).

Then you can use the trained method on new arbitrary structures. If you've done everything right you get good, or good enough results, but much much faster.

At a high level It's the same pipeline as in all ML. But some aspects are different, e.g. unlike image recognition you can generate training data on the fly by running more DFT simulations

[fennecfoxy]

That's pretty cool! It seems like most of ML is just creating a higher dimensional representation of the problem space during training and then exploring that during inference.

I suppose your process would be using ML to get pointed in the "right direction" and then confirming the models theories using the expensive method?

[dmarchand90]

Yeah exactly like this. It is a subtle art of validating in small scale a method you would later use at large scale.

[aflip]

ibh i didn't understand most of that but sounds exciting.

[dmarchand90]

We want to do computer experiments instead of real life experiments to discover or improve chemicals and materials. The current way of doing computer experiments is really really slow and takes a lot of computers. We now have much faster ways of doing the same computer experiments by first doing it the slow way a bunch of time to train an machine learning model. Then, with the trained model, we can do the same simulations but way way faster. Along the way there are tons of technical challenges that don't show up in LLMs or Visual machine learning.

If there is anything unclear you're interested in just let know. In my heart I feel I'm still just a McDonald's fry cook and feel like none of this is as scary as it might seem :)