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Abdulmonem Obaied

Publications and source records attributed to Abdulmonem Obaied.

2 recordsLinked to original sources

Thermodynamic Modeling of Pure Elements from 0 K with Uncertainty Quantification using PyCalphad and ESPEI

Thermodynamic modeling of pure elements is the foundation of the CALPHAD modeling of engineering materials. Recently, multiple physics-based models have been proposed to describe Gibbs energy of pure elements down to 0 K, extending from 298.15 K in the current CALPHAD modeling. To enable their systematic and quantitative comparison and adoption, those thermodynamic models of pure elements are implemented into the open-source software packages PyCalphad and ESPEI in the present work for evaluation of model parameters and model fitness. PyCalphad and ESPEI are suitable tools for implementation of these models for high throughput CALPHAD modeling of multicomponent materials. Particularly, Markov Chain Monte Carlo used in ESPEI allows for uncertainty quantification of model parameters and model predictions. Through the remodeling of 41 pure elements, the present work demonstrates the quantitative comparison of modeling of pure elements with different models and enables the efficient development of multicomponent systems with continuously improved CALPHAD description of pure elements.

cond-mat.mtrl-sci

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

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.

cond-mat.mtrl-sci