[ylem]

There are a lot of cool advances in AI and physics. In my particular field of condensed matter physics, a number come to mind. One is trying to automatically extract synthesis recipes from the literature. Imagine that you want to see how people have synthesized a given solid state compound. Then searching through the literature can be painful. A great collaboration from MIT/Berkeley did this using NLP. I don't know what blood oaths they signed, but they were able to obtain a huge corpus of articles. But, how to know if an article contains a synthesis recipe? They set up their internal version of Mechanical Turk and had their students label a number of articles. Then they had to find the recipes, represent them as a DAG, etc. They have now incorporated the result with the Materials project (https://materialsproject.org/apps/synthesis/#).

There are groups that are using graph neural networks to understand statistical mechanics and microscopy. There are also a number of groups working on trying to automate synthesis (most of it is Gaussian process based, a handful of us are trying reinforcement learning--it's painful). On the theory side, there is work speeding up simulation efforts (ex. DFT functionals) as well as determining if models and experiment agree (Eun Ah Kim rocks!).

Outside of my field, there has been a push with Lagrangian/Hamiltonian NNs that is really cool in that you get interpretability for "free" when you encode physics into the structure of the network. Back to my field, Patrick Riley (Google) has played with this in the context of encoding symmetries in a material into the structure of NNs.

There are of course challenges. In some fields, there is a huge amount of data--in others, we have relatively small data, but rich models. There are questions on what are the correct representations to use. Not to mention the usual issues of trust/interpretability. There's also a question of talent given opportunities in industry.