Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling

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.

Automated summary

Atomistic simulations are important in materials modeling, and the choice of interatomic potentials (IPs) greatly affects the accuracy of the predictions. Uncertainty quantification (UQ) is a new tool for assessing the reliability of these simulations. The OpenKIM project aims to standardize the study of IPs and enable transparent research, and the KLIFF Python package provides tools for fitting IP parameters. This paper introduces a UQ extension to KLIFF, focusing on parameter variations and inadequacy of the IP's functional form.