Uncertainty quantification for classical effective potentials: an extension to potfit

Abstract

Effective potentials are an essential ingredient of classical molecular dynamics (MD) simulations. Little is understood of the consequences of representing the complex energy landscape of an atomic configuration by an effective potential or force field containing considerably fewer parameters. The probabilistic potential ensemble method has been implemented in the potfit force matching code. This introduces uncertainty quantification into the interatomic potential generation process. Uncertainties in the effective potential are propagated through MD to obtain uncertainties in quantities of interest, which are a measure of the confidence in the model predictions. We demonstrate the technique using three potentials for nickel: two simple pair potentials, Lennard-Jones and Morse, and a local density dependent embedded atom method (EAM) potential. A potential ensemble fit to density functional theory (DFT) reference data is constructed for each potential to calculate the uncertainties in lattice constants, elastic constants and thermal expansion. We quantitatively illustrate the cases of poor model selection and fit, highlighted by the uncertainties in the quantities calculated. This shows that our method can capture the effects of the error incurred in quantities of interest resulting from the potential generation process without resorting to comparison with experiment or DFT, which is an essential part to assess the predictive power of MD simulations.

10 pages, 3 figures

References in corpus (5)

• Potfit: effective potentials from ab-initio data
• Effective potentials for quasicrystals from ab-initio data
• Evaluation of Copper, Aluminum and Nickel Interatomic Potentials on Predicting the Elastic Properties
• A critical review of statistical calibration/prediction models handling data inconsistency and model inadequacy
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Cited by in corpus (7)

• KLIFF: A framework to develop physics-based and machine learning interatomic potentials
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• EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular Dynamics
• Uncertainty Quantification in Atomistic Simulations of Silicon using Interatomic Potentials
• Functional uncertainty quantification for isobaric molecular dynamics simulations and defect formation energies
• Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling
• Data-driven Uncertainty Quantification for Systematic Coarse-grained Models