The authors propagate MCMC-quantified CALPHAD parameter uncertainty through an elasto-chemical phase-field model of Mg2(SixSn1-x) and use machine learning to map the resulting microstructure distributions.
ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg
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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 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.
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cond-mat.mtrl-sci 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-field Model
The authors propagate MCMC-quantified CALPHAD parameter uncertainty through an elasto-chemical phase-field model of Mg2(SixSn1-x) and use machine learning to map the resulting microstructure distributions.