ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg

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.

26 pages, 6 figures

References in corpus (4)

• emcee: The MCMC Hammer
• Simulation of Cu-Mg metallic glass: Thermodynamics and Structure
• Quantified Uncertainty in Thermodynamic Modeling for Materials Design
• Building effective models from sparse but precise data

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