Uncertainty quantification for classical effective potentials: an extension to potfit
| Autor: | Sarah Longbottom, Peter Brommer |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Materials science
FOS: Physical sciences Interatomic potential 02 engineering and technology 01 natural sciences Force field (chemistry) Molecular dynamics Lattice constant 0103 physical sciences QD General Materials Science Statistical physics Uncertainty quantification QC 010302 applied physics Condensed Matter - Materials Science Model selection Probabilistic logic Materials Science (cond-mat.mtrl-sci) Computational Physics (physics.comp-ph) 021001 nanoscience & nanotechnology Condensed Matter Physics Computer Science Applications Mechanics of Materials Modeling and Simulation Density functional theory 0210 nano-technology Physics - Computational Physics |
| Zdroj: | Modelling and Simulation in Materials Science and Engineering. 27:044001 |
| ISSN: | 1361-651X 0965-0393 |
| DOI: | 10.1088/1361-651x/ab0d75 |
| Popis: | 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. Comment: 10 pages, 3 figures |
| Databáze: | OpenAIRE |
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