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

Published online by Cambridge University Press:  04 June 2019

Brandon Bocklund Open the ORCID record for Brandon Bocklund [Opens in a new window] ,
Richard Otis ,
Aleksei Egorov ,
Abdulmonem Obaied ,
Irina Roslyakova  and
Zi-Kui Liu
Brandon Bocklund*
Affiliation:
Department of Materials Science & Engineering, Pennsylvania State University, University Park, PA, 16802, USA
Richard Otis
Affiliation:
Engineering and Science Directorate, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Aleksei Egorov
Affiliation:
ICAMS, Ruhr-University Bochum, Universitätstr. 150, 44801, Bochum, Germany
Abdulmonem Obaied
Affiliation:
ICAMS, Ruhr-University Bochum, Universitätstr. 150, 44801, Bochum, Germany
Irina Roslyakova
Affiliation:
ICAMS, Ruhr-University Bochum, Universitätstr. 150, 44801, Bochum, Germany
Zi-Kui Liu
Affiliation:
Department of Materials Science & Engineering, Pennsylvania State University, University Park, PA, 16802, USA
*
Address all correspondence to Brandon Bocklund <bjb54@psu.edu>
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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 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.

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Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 618 - 627
Copyright
Copyright © Materials Research Society 2019 

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