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arXiv · 1902.01269

ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg

Brandon Bocklund·Richard Otis·Aleksei Egorov·Abdulmonem Obaied·Irina Roslyakova·Zi-Kui Liu
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Abstract

The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refine the model parameters using phase equilibrium data through Bayesian optimization within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu-Mg system down to 0 K using unary descriptions based on segmented regression. The model parameter uncertainties are quantified and propagated to the Gibbs energy functions.

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Brandon Bocklund, Richard Otis, Aleksei Egorov, Abdulmonem Obaied, Irina Roslyakova, Zi-Kui Liu. 2019-02-04. ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg. https://doi.org/10.1557/mrc.2019.59

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