ICAMS / Interdisciplinary Centre for Advanced Materials Simulation

Atomistic Modelling and Simulation (AMS)

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

B. Bocklund, R. Otis, A. Egorov, A. Obaied, I. Roslyakova, Z. Liu.

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.

DOI: 10.1557/mrc.2019.59
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