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

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)618-627
Number of pages10
JournalMRS Communications
Volume9
Issue number2
DOIs
StatePublished - Jun 1 2019

All Science Journal Classification (ASJC) codes

  • Materials Science(all)

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