  Atomistic simulations are an important tool in materials modeling.
Interatomic potentials (IPs) are at the heart of such molecular models, and the
accuracy of a model's predictions depends strongly on the choice of IP.
Uncertainty quantification (UQ) is an emerging tool for assessing the
reliability of atomistic simulations. The Open Knowledgebase of Interatomic
Models (OpenKIM) is a cyberinfrastructure project whose goal is to collect and
standardize the study of IPs to enable transparent, reproducible research. Part
of the OpenKIM framework is the Python package, KIM-based Learning-Integrated
Fitting Framework (KLIFF), that provides tools for fitting parameters in an IP
to data. This paper introduces a UQ toolbox extension to KLIFF. We focus on two
sources of uncertainty: variations in parameters and inadequacy of the
functional form of the IP. Our implementation uses parallel-tempered Markov
chain Monte Carlo (PTMCMC), adjusting the sampling temperature to estimate the
uncertainty due to the functional form of the IP. We demonstrate on a
Stillinger--Weber potential that makes predictions for the atomic energies and
forces for silicon in a diamond configuration. Finally, we highlight some
potential subtleties in applying and using these tools with recommendations for
practitioners and IP developers.
