Frequentist Approach to Uncertainty Quantification of Interatomic Models in OpenKIM Database

Interatomic models (IMs) are used in molecular modeling to predict material properties of interest. The development of a single IM can take anywhere from several months to years and relies on expert intuition, and yet these potentials are usually only valid for a particular application of interest. Extending existing IMs to new applications is an active area of research. Quantifying the uncertainty of an IM can tell us how much we can trust the predictions it makes. I take a frequentist approach to uncertainty quantification. I calculate the profile likelihood of the parameters in the IM to identify regions of the parameter space that are statistically consistent with the data on which it is trained. I demonstrate this method on Lennard-Jones and Morse potentials fit to triclinic crystal configurations from the OpenKIM database. Results indicate that these models are "sloppy" in some of their parameters, i.e., likelihood surfaces have long, narrow canyons and broad, flat plateaus. I discuss implications of sloppiness for molecular modeling and potential extensions to more complex potentials.