Lately, I've become quite intrigued by the recent advances in machine learning forcefields within the field of computational chemistry. I've developed a curiosity about training a simple forcefield on a transition metal bulk mono-element.
I've been pondering the best approach to train the model. Initially, the idea that came to mind was training it on different volumes, elementary transformations (such as shear and strain), and normal modes (and combinations of these three).
To gain more insights, I've been reading the literature, and it appears that various approaches are employed:
Training on completely random structures: There are different methods available, but the underlying result is that you predominantly train on structures that may not have experimental relevance and, in some cases, aren't physically plausible. One potential advantage is that your forcefield could become robust against diverse scenarios. However, the way I understand it, there is a possible accuracy tradeoff on important properties for real systems?
Training on Molecular Dynamics data of known systems (FCC here): This particular approach puzzles me a bit, as it seems to be the opposite of the previous one. Essentially, you train the model on correlated and biased data, often at a specific temperature. At first glance, this doesn't appear to be advantageous over randomly generated displacements (uncorrelated)?
At the moment, my training approach involves scanning different volumes and elementary transformations, while incorporating random displacements of varying intensities.
Obviously, how to train is heavily system and potential dependent. I understand that this is quite a vague question. I would be grateful if someone could give me an insight, or (even better) correct me if I wrote anything wrong.