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

Autor:	Bocklund, Brandon, Otis, Richard, Egorov, Aleksei, Obaied, Abdulmonem, Roslyakova, Irina, Liu, Zi-Kui
Rok vydání:	2019
Předmět:	Condensed Matter - Materials Science Physics - Computational Physics
Zdroj:	MRS Communications 9(2) (2019) 618-627
Druh dokumentu:	Working Paper
DOI:	10.1557/mrc.2019.59
Popis:	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. Comment: 26 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1902.01269 Zobrazit plný text záznamu View this record from Arxiv