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        <datestamp>2026-09-15T07:55:08Z</datestamp>
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          <dc:title>Data for Uncertainty quantification for classical effective potentials: an extension to potfit</dc:title>
          <dc:creator>Sarah Longbottom (24916768)</dc:creator>
          <dc:creator>Peter Brommer (1619815)</dc:creator>
          <dc:subject>Molecular dynamics -- Simulation methods</dc:subject>
          <dc:subject>Density functionals</dc:subject>
          <dc:subject>Nickel</dc:subject>
          <dc:subject>Migrated from ePrints</dc:subject>
          <dc:description>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.&lt;br&gt;&lt;br&gt;This dataset contains the necessary input files and basic instructions to reproduce the results in the article: Longbottom et al 2019 Modelling Simul. Mater. Sci. Eng. https://doi.org/10.1088/1361-651X/ab0d75. The results and included files have been used to illustrate the process of quantifying uncertainties in quantities of interest (lattice constant, elastic constants and thermal expansion coefficient) predicted by three interatomic potential models (LJ, Morse and EAM), fitted to a basic Nickel reference dataset.  The files have been organised in numerically ascending directories indicating the workflow of the data generation. A brief description of each is as follows:  0_reference_data: This directory contains all the necessary input files required to create the 23-snapshot reference dataset to which the potential models are fitted. All but the geometry optimised snapshot use the LAMMPS molecular dynamics software to create trajectories, with single point energy calculations of the resulting configurations performed using the CASTEP density fictional theory code. The geometry optimised configuration was also created using CASTEP. Additional details of each type of snapshot are detailed in README.TXT files within the relevant folders.  1_potential_fit: The reference data created in the previous folder is amalgamated to create the input file Ni_AAH.config. The interatomic potential models are fitted to the reference data using the potfit force matching code. Further details are included in a README.txt file.  2_ensemble_generation: The potential ensembles used to quantify the uncertainties in the quantities of interest are generated using potfit with the newly implemented ensemble generation functionality outlined in the related article. The ensembles used in the article are included in the following directory and following the naming convention “Ni_*_23%_full_downsample.dat”.  3_lattice_constant_and_ECs: The lattice constants and elastic constants are calculated for each member of each ensemble. The process is automated by the “processing_EAM_EC_C11_C12_C44” bash script, which also details the adjustments required to work with each of the three model ensembles. Potfit is used to output the necessary potential format, and LAMMPS (compiled with the manybody option) is used to simulate the quantities of interest. The results are included in the “elastic_constants_C11_C12_C44.out_*” files.  4_thermal_expansion: The thermal expansion calculations were performed on the a (slurm) batch processing system using the automated submission scripts “thermal_expansion-*-eam.pbs”. The simulations use LAMMPS and also require python. The results used in the article are included in the “*_TE.out” files.</dc:description>
          <dc:date>2026-09-15T07:55:08Z</dc:date>
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          <dc:identifier>10.82444/warw.33777088.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Data_for_Uncertainty_quantification_for_classical_effective_potentials_an_extension_to_potfit/33777088</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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