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

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)
JournalMRS Communications
DOIs
StatePublished - Jan 1 2019

Fingerprint

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.
@article{1ac1e768e4044ce5b35f401697f1265e,
title = "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 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.",
author = "Brandon Bocklund and Richard Otis and Aleksei Egorov and Abdulmonem Obaied and Irina Roslyakova and Zi-kui Liu",
year = "2019",
month = "1",
day = "1",
doi = "10.1557/mrc.2019.59",
language = "English (US)",
journal = "MRS Communications",
issn = "2159-6859",
publisher = "Cambridge University Press",

}

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, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification

T2 - Application to Cu-Mg

AU - Bocklund, Brandon

AU - Otis, Richard

AU - Egorov, Aleksei

AU - Obaied, Abdulmonem

AU - Roslyakova, Irina

AU - Liu, Zi-kui

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85066017363&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066017363&partnerID=8YFLogxK

U2 - 10.1557/mrc.2019.59

DO - 10.1557/mrc.2019.59

M3 - Article

JO - MRS Communications

JF - MRS Communications

SN - 2159-6859

ER -