@spozdn Best Poster @unifrChemistry Great work!!

We are using a simple architecture, augmented with attention, so these models scale very well (5x faster than MACE with a system of 10k atoms). In particular - our models are NOT equivariant. Our models hopefully show that this is a modeling choice, not a requirement for Matsci.

I gave a lecture at @Stanford CS 25. Lecture video: AI is moving so fast that it's hard to keep up. Instead of spending all our energy catching up with the latest development, we should study the change itself. First step is to identify and understand the dominant driving force behind the change. For AI, a single driving force stands out; exponentially cheaper compute and scaling of progressively more end-to-end models to leverage that compute. However this doesn’t mean we should blindly adopt the most end-to-end approach because such an approach is simply infeasible. Instead we should find an “optimal” structure to add given the current level of 1) compute, 2) data, 3) learning objectives, 4) architectures. In other words, what is the most end-to-end structure that just started to show signs of life? These are more scalable and eventually outperform those with more structures when scaled up. Later on, when one or more of those 4 factors improve (e.g. we got more compute or found a more scalable architecture), then we should revisit the structures we added and remove those that hinder further scaling. Repeat this over and over. As a community we love adding structures but a lot less for removing them. We need to do more cleanup. In this lecture, I use the early history of Transformer architecture as a running example of what structures made sense to be added in the past, and why they are less relevant now. I find comparing encoder-decoder and decoder-only architectures highly informative. For example, encoder-decoder has a structure where input and output are handled by separate parameters whereas decoder-only uses the shared parameters for both. Having separate parameters was natural when Transformer was first introduced with translation as the main evaluation task; input is in one language and output is in another. Modern language models used in multiturn chat interfaces make this assumption awkward. Output in the current turn becomes the input of the next turn. Why treat them separately? Going through examples like this, my hope is that you will be able to view seemingly overwhelming AI advances in a unified perspective, and from that be able to see where the field is heading. If more of us develop such a unified perspective, we can better leverage the incredible exponential driving force! Slides: