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.
@article{arxiv.1902.01269,
title = {ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg},
author = {Brandon Bocklund and Richard Otis and Aleksei Egorov and Abdulmonem Obaied and Irina Roslyakova and Zi-Kui Liu},
journal= {arXiv preprint arXiv:1902.01269},
year = {2019}
}