Machine learning is an increasingly common tool for developing force fields for molecular dynamics simulations. It's not totally clear what should be considered a machine-learning potential, but let's just say a machine learning potential is one which has many, many parameters which do not have any obvious physical interpretation. Any kind of neural network, Gaussian process, or permutationally invariant polynomial model, I would consider as an ML potential for this question.
I have had quite a few conversations in which people say things along the lines of "machine learning potentials are slower than molecular mechanics potentials." I've observed this to be true for certain ML potentials I've used, but I haven't seen any real benchmarking of this.
Are there references which provide good comparisons of the speed of ML potentials to comparable classical force fields? I have seen a few papers comparing the accuracy of, for instance, classical versus ML water models, but not a corresponding comparison of the speeds of these models.
Here is a recent paper which augments a classical force field with an ML force field to kind of get the best of both worlds (speed and accuracy) to a certain extent. I still don't see a real time comparison though.