>Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else.
Ideally in science you would like to use literally anything else other than ML if possible, fitting models come with their own challenges and neural networks are even more of a nightmare. Understanding the world well enough to hard code a rule is always preferable to fitting to some data and hoping the model will come up with a rule. While there has been some attempts to use ML for feature detection it then takes a lot of experimenting to generally show if it detected signal or just some noise in your data.
Most of the things that would accelerate science would either require AI much more complex than we currently have (basically replacing lab assistants with AI) or are incredible research undertakings in their own right like Alpha Fold, Deep Potential Neural Networks, etc.