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Bayesian automated weighting of aggregated DFT, MD, and experimental data for candidate thermodynamic models of aluminum with uncertainty quantification

Published

Author(s)

Francesca Tavazza, Chandler A. Becker, Ursula R. Kattner, Joshua Gabriel, Noah Palson, Thien Duong, Marius Stan

Abstract

Atomic-scale modeling methods such as density functional theory (DFT) and molecular dynamics (MD) can predict the thermodynamic properties of materials at a lower cost than experimental measurements. However, their regular usage in thermodynamic model construction is hampered by the lack of quantitative agreement with experimental measurements and the lack of uncertainty estimates on the data. To make regular usage of this atomistic simulation data, it is important to assess whether the atomistic simulation datasets by themselves or in combination with experimental measurements, result in the same physics-informed models best supported by experimental measurements alone. In this work, models of Aluminum thermodynamic properties are discussed using three data sources: atomistic calculations (DFT and MD), experiments, and a combination of atomistic calculations and experiments. The study shows that, after ensuring self-consistency in predicting key invariant points, both experimental measurements and atomistic calculations significantly contribute to the optimal model.
Citation
ACTA Materialia
Volume
20

Keywords

thermodynamics, UQ, Aluminum, Calphad, MD, DFT

Citation

Tavazza, F. , Becker, C. , Kattner, U. , Gabriel, J. , Palson, N. , Duong, T. and Stan, M. (2021), Bayesian automated weighting of aggregated DFT, MD, and experimental data for candidate thermodynamic models of aluminum with uncertainty quantification, ACTA Materialia, [online], https://doi.org/10.1016/j.mtla.2021.101216, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932392 (Accessed April 18, 2024)
Created December 1, 2021, Updated February 22, 2023