Good afternoon to everyone!
I’m new in the ML potentials creation and I have few questions. I really appreciate if anyone may help me.
I’m interested in some details of MLP training datasets creation. It’s obviously that a dataset containing wide range of structures has to be prepared for a training. But how definitely the structures are chosen?
I mean, it’s logical (?) to take a pressure-temperature phase diagram and mark all the transition points and create structures at the points and near the points for the dataset. But how many states from the vicinity of a transition point should be taken? And what is the vicinity - I mean, how big dT and dP near the point it should be? And also the parts inside the transition lines also have to be covered well. So how usually is this done? All is divided with lines of constant pressure and temperature with step, for example, N GPa and M Kelvin, and then states at the cross points of the lines are created? If it is literally the block division then what pressure step and what temperature step are enough? Or is it done somehow different?
Thank you for your help!