期刊
MRS COMMUNICATIONS
卷 9, 期 2, 页码 618-627出版社
SPRINGER HEIDELBERG
DOI: 10.1557/mrc.2019.59
关键词
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资金
- NSF National Research Trainee Fellowship [DGE-1449785]
- NASA Space Technology Research Fellowship [80NSSC18K1168]
- NSF grant [ACI-1548562]
- NASA's Science Mission Directorate [80NM0018D0004]
- Space Technology Office at the Jet Propulsion Laboratory, California Institute of Technology
- Collaborative Research Center Superalloys Single Crystal of the German Research Foundation (DFG) [TR-103]
- Sino-German Cooperation Group
- NASA's Space Technology Mission Directorate through the Game Changing Development program under Prime Contract [80NM0018D0004]
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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