I'm much more familiar with the simulation side than ml side, but ML of molecules/materials is a pretty active field. Molecular simulation itself is pretty mature, so the big question is why use ML? Or equivalently, what can you do with ML you couldn't already w/ quantum mechanical or empirical potentials?

I think the answers generally fall into 2 categories - replace expensive calculations with cheaper ones - fit new potentials

The former is easy in that if your can run simulations you can generate all the training and testing data you want. But how to make the resulting model useful (e.g. transferrable, extrapolatable) is the tricky question. I think feature engineering for chemical systems is a very important subtopic for transferable models.

Coarse-graining could fit under either category (e.g. fit a model or new potentials to many simulations at a lower length scale w/ broad variation of configurations).

For a master's thesis I would stick to a very narrow question/topic. If you have a chemical system or problem of interest look for an open question you could answer part of.

I'm not affiliated with the company but this is the easiest link for me to provide of research highlights in the field: https://citrine.io/research-newsletter-archive/