CALPHAD Uncertainty Quantification and TDBX

Yu Lin, Abhinav Saboo, Ramón Frey, Sam Sorkin, Jiadong Gong, Gregory B. Olson, Meng Li, Changning Niu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

CALPHAD uncertainty quantification (UQ) is the foundation of materials design with quantified confidence. We report a framework and software packages to enable CALPHAD UQ assessment and calculation using commercial CALPHAD software (Thermo-Calc). This Bayesian inference framework is coupled with a Markov chain Monte Carlo algorithm to establish uncertainty traces with a given thermodynamic database file (TDB) and corresponding experimental data points. This general framework is demonstrated with the Ni–Cr binary system. The algorithm is firstly validated on synthetic data with known ground truth. Then it is applied to real experimental data to generate posterior traces. We develop a file format named TDBX, which provides a single source of truth by combining the original TDB content and the traces for each assessed Gibbs energy parameter. CALPHAD UQ calculations are performed based on the TDBX file, from which uncertainties for phase boundaries, enthalpy curves, and solidification range are collected as examples of basic design parameters. This TDBX file with corresponding scripts are made open-source. The combination of CALPHAD UQ assessments and calculations connected by TDBX supports uncertainty-assisted modeling, enabling the integrated application of modern design with uncertainty methodologies to computational materials design.

Original languageEnglish (US)
Pages (from-to)116-125
Number of pages10
JournalJOM
Volume73
Issue number1
DOIs
StatePublished - Jan 2021

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'CALPHAD Uncertainty Quantification and TDBX'. Together they form a unique fingerprint.

Cite this