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.

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    $\begingroup$ +10. A very interesting question indeed! And welcome to the site !!! $\endgroup$ – Nike Dattani Sep 1 '20 at 19:57

We performed some timing benchmarks as part of our recent paper, albeit not on molecular dynamics:

"Assessing conformer energies using electronic structure and machine learning methods" Int J Quantum Chem. 2020; 121:e26381

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It was a bit controversial, since we compared single-core CPU times and not in batch mode. Once the ML method runs the model, it's faster - although we saw speedups of ~70-100x for both ML and force field methods.

No doubt ML methods are faster on GPU, but tuned force fields are as well.

It seems hard to imagine an ML method that's truly faster than a good implementation of a force field. Most force field terms are intentionally designed to use only a few arithmetic operations (e.g. bonding terms require distance and a harmonic potential). As such, they can be highly optimized for both GPU and CPU implementations.

On the other hand, ML methods can clearly do a more accurate job with empirically fitting (e.g, non-bonded interactions). I suspect more hybrid methods will appear over time.


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