Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling

TL;DR

This paper extends the OpenKIM framework with a UQ toolkit using PTMCMC to assess uncertainties in interatomic potentials, demonstrated on silicon energy predictions.

Contribution

It introduces a novel UQ extension to KLIFF, enabling uncertainty analysis of IP parameters and functional form within OpenKIM.

Findings

Demonstrated uncertainty quantification on silicon using PTMCMC.

Highlighted subtleties and best practices for applying UQ tools.

Enhanced OpenKIM with a new UQ toolkit for molecular modeling.

Abstract

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.