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Automated CALPHAD assessment of Cu-Mg system with ESPEI and segmented regression model from 0K

Date: 28.06.2018
Place: Sino-German Symposium: Modelling of thermodynamics, kinetics and microstructures in alloys, Bochum, Germany

Brandon Bocklund, Department of Materials Science and Engineering, Pennsylvania State University, University Park, USA
Richard Otis, Engineering and Science Directorate, California Institute of Technology, Pasadena, CA, USA
Aleksei Egorov
Abdulmonem Obaied
Irina Roslyakova
Zi-Kui Liu, Department of Materials Science and Engineering, The Pennsylvania State University, University Park, USA

Thermodynamic modeling using the CALPHAD (=CALculation of PHAse Diagram) method is one of the key component for a successful and robust design of new materials. Recent implementation of the CALPHAD method, however, is lacking strategies and tools for straightforward implementation of the new models and new experimental and DFT data into existing databases. The development of data repositories and automation tools together with robust mathematical models are three main components to improve the efficiency of existing CALPHAD modeling approach, especially for updating multicomponent databases. In this work, an approach for automated assessment will be demonstrated on the Cu-Mg binary system (Figure 1). The proposed solution is based on combination of open-source software ESPEI (= Extensible Self-optimizing Phase Equilibria Infrastructure) [1, 2] and newly proposed physically-based segmented regression (SR) model [3], which allows to perform thermodynamic calculations from 0K. ESPEI is an open-source Python-based software for automated thermodynamic database development within the CALPHAD method. It uses the pycalphad software package [4] for calculating Gibbs free energies of thermodynamic models to rapidly develop and modify databases using a combination of first-principles and experimental thermochemical and phase equilibria data. Unlike traditional database development, ESPEI uses Markov chain Monte Carlo (MCMC) to optimize and quantify the uncertainty for all model parameters simultaneously.

References:
[1] Shang, S. L., Wang, Y., & Liu, Z. K. ESPEI: Extensible, self-optimizing phase equilibrium infrastructure for magnesium alloys. In Magnesium Technology (2010) pp. 617-622
[2] www.espei.org
[3] I. Roslyakova, B. Sundman, H. Dette, L. Zhang, and I. Steinbach. Modeling of Gibbs energies of pure elements down to 0 K using segmented regression. CALPHAD Journal, 55, 2016.
[4] Otis, R. and Liu, Z.-K. pycalphad: CALPHAD-based Computational Thermodynamics in Python, J. Open Res. Softw. 5 (2017)

Supporting information:

b.bocklund2018-7-7209.pdf
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