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 journalArticle

1 Citation (Scopus)

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

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Thermodynamics
Gibbs free energy
Software packages
Phase equilibria
Parameter estimation
Markov processes
Learning systems
Uncertainty

All Science Journal Classification (ASJC) codes

  • Materials Science(all)

Cite this

Bocklund, Brandon ; Otis, Richard ; Egorov, Aleksei ; Obaied, Abdulmonem ; Roslyakova, Irina ; Liu, Zi Kui. / ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification : Application to Cu-Mg. In: MRS Communications. 2019 ; Vol. 9, No. 2. pp. 618-627.
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ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification : Application to Cu-Mg. / Bocklund, Brandon; Otis, Richard; Egorov, Aleksei; Obaied, Abdulmonem; Roslyakova, Irina; Liu, Zi Kui.

In: MRS Communications, Vol. 9, No. 2, 01.06.2019, p. 618-627.

Research output: Contribution to journalArticle

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