I know the general question of machine learning in computational chemistry has been already raised here: What is the current status of machine learning applied to materials or molecular systems?
However, still, I'm curious, what are the pros and cons of ML force fields for MD simulations.
Classical empirical potential models are fast but they are incorrect or cannot predict specific chemistry and bond formation/dissociation. Reactive potentials have some accuracy (depending on parameters) but they are slow.
Then... where is the position of ML potentials? Are they accurate? Or fast? Or both? I read some papers by Podryabinkin et. al. and by Deringer et al..
However, as a person who never tried ML potentials before, it is really hard to judge or feel the state of the ML force field.
So, if anyone tried various interatomic potentials including ML force fields (in Gromacs or Lammps or any platforms) may I ask how much accurate/fast are they, and what is the advantage/disadvantage of ML force field? Is this easy/hard to learn, or easy/hard to get "good parameter", etc..
- Podryabinkin, E. V.; Tikhonov, E. V.; Shapeev, A. V.; Oganov, A. R. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Phys. Rev. B 2019, 99 (6), No. 064114. DOI: 10.1103/PhysRevB.99.064114.
- Deringer, V. L.; Caro, M. A.; Csányi, G. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Adv. Mater. 2019, 31 (46), 1902765. DOI: 10.1002/adma.201902765.