Hi everyone,
I am generating training data for an on-the-fly MLFF (ML force field) using ab initio MD for a KTaO₃ system. The goal is to sample a sufficiently broad configuration space while maintaining stable and physically meaningful dynamics.
In this context, I would appreciate guidance on choosing appropriate MD parameters. For example, I am currently using values such as:
LANGEVIN_GAMMA = 10.0 10.0 10.0
LANGEVIN_GAMMA_L = 10.0
PMASS = 100
POTIM = 2.0
My questions are:
Is there a recommended strategy or physical criterion for selecting these parameters when the primary aim is to efficiently span configuration space for MLFF training?
How sensitive is the quality/diversity of the training data to the choice of friction coefficients (LANGEVIN_GAMMA) and time step (POTIM)?
Are there best practices (or typical parameter ranges) that people follow for on-the-fly MLFF generation in oxide perovskites or similar systems?
Any advice or references would be greatly appreciated.
Thanks
Dominic

