2019/02/04 by Brandon Bocklund, Richard Otis, Aleksei Egorov +3 · 1 citation
Physics and Astronomy · #cond-mat.mtrl-sci #physics.comp-ph
paper · pdf · doi:10.1557/mrc.2019.59
published as MRS Communications 9(2) (2019) 618-627 · 26 pages, 6 figures
arxiv created 2019/02/04 · arxiv updated 2019/07/30
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.