ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu–Mg
Bocklund, Brandon, Otis, Richard, A. Egorov, A. Obaied, I. Roslyakova, Liu, Zi-Kui.
MRS Communications: Artificial Intelligence Research Letter, 9, 618-627, (2019)
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 refines the model parameters using phase equilibrium data through Bayesian parameter estimation 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.
Cite as: https://www.cambridge.org/core/journals/mrs-communications/article/espei-for-efficient-thermodynamic-database-development-modification-and-uncertainty-quantification-application-to-cumg/24D16AF6CEB9C9EB426616CABA4B5346