This is a huge topic in applying ML in physics and chemistry where we already have a lot of prior detailed knwoledge about the systems we want to describe and it would be silly not to build it into the ML models.
People now try to use ML anywhere and everywhere so it's wild west a little. Three examples: [1] uses a standard neural net to represent a many-body wave function, with all the machinery of quantum mechanics on top of that, and reinforcement learning to find the true ground state. [2] uses a handcrafted neural net, which by construction already takes advantage of a lot of prior knowledge, to directly predict molecular energies. [3] uses a simple kernel ridge regression coupled with a sophisticated handcrafted scheme to automatically construct a good basis (set of features) for a given input, to predict molecular energies.
In all these cases, the ML itself is not the target problem, but only a tool, and most effort goes into figuring out where exactly to use ML as a part of a larger problem, and how to encode prior knowledge, either via feature construction or neural net handcrafting.