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Thanks @lan496 for this very good question! I agree that computing k_SRME for many different finite displacements may provide additional information, however there are a few things that one should keep in mind. Computing k_SRME for many different displacements would also require having reference DFT data computed for many different displacements. In DFT, the finite displacements have to be chosen within a physically and numerically sensible range of 0.005 Å - 0.05 Å (this range ensures that energy perturbations are numerically large enough to be above the DFT accuracy, and physically small enough to be treated as perturbations). In DFT, varying the finite displacement within this range produces, almost without exception, unimportant differences (~<3%) on vibrational and thermal properties within Born-Oppenheimer DFT. These 3% variations are much smaller than those probed by k_SRME between DFT and MLP. Since a good MLP is expected to reproduce the DFT PES, for a fixed finite displacement a good MLP is expected to reproduce the reference DFT value computed with the same displacement. In other words, having a low k_SRME is a necessary condition for a MLP to be accurate. Whenever a MLP predicts vibrational properties with a dependence on the displacement larger than that observed in reference DFT data, it means that the MLP fails to reproduce the smoothness of the DFT PES.
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(This is a continuation from the discussion in my Linkedin's post)
Matbench-discovery is now working on integrating the lattice thermal conductivity (LTC) task by k_SRME. This new benchmark definitely adds a new dimension to evaluating uNNPs. However, I've recently noticed that many uNNPs have a non-negligible dependency on LTC predictions from displacement distances (see details in our group's blog post). Therefore, I believe that testing with a single displacement distance is insufficient for properly evaluating uNNPs by their metrics.
@janosh thinks testing more than one displacement distance can be informative, similar to the approach used in the geo-opt task (comment). I also think it would be beneficial to test with several displacement distances.
If we use multiple displacement distances, how should we select them? Or, from a broader perspective, how can we quantify the smoothness of the uNNP PES based on this benchmark?
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