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Summary

Integrated computational materials engineering (ICME) relies on the CALPHAD method for materials development. This review emphasizes critically evaluating experimental data to ensure reliable CALPHAD function development and accurate materials property predictions.

Keywords:
CALPHADcomputational datacomputational thermodynamicsexperimental dataphase equilibria datathermochemical datathermophysical data

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Area of Science:

  • Materials Science
  • Computational Materials Engineering
  • Thermodynamics

Background:

  • Computational methods are crucial for materials development, leading to Integrated Computational Materials Engineering (ICME).
  • The CALPHAD (Calculation of Phase Diagrams) method is a foundational pillar of ICME.
  • CALPHAD models thermodynamic properties and phase diagrams using extrapolation from lower-order systems.

Purpose of the Study:

  • To provide an overview of the CALPHAD method and its underlying models.
  • To summarize the essential experimental data required for CALPHAD function development.
  • To outline criteria for critically evaluating experimental data used in CALPHAD.

Main Methods:

  • Review of CALPHAD methodology and thermodynamic modeling principles.
  • Identification and categorization of necessary experimental data for CALPHAD.
  • Development of criteria for assessing the quality and reliability of experimental data.

Main Results:

  • The CALPHAD method enables property prediction for complex systems through extrapolation.
  • CALPHAD functions are heavily dependent on the quality of input experimental data.
  • A systematic approach to data evaluation is essential for CALPHAD accuracy.

Conclusions:

  • Reliable CALPHAD calculations necessitate rigorous evaluation of experimental data.
  • Critical assessment of data ensures the integrity of CALPHAD models and ICME applications.
  • This review provides a framework for data evaluation in CALPHAD-based materials research.