Events
Place: 17th Discussion Meeting on Thermodynamics of Alloys (TOFA), Bad Staffelstein, Germany
Abdulmonem Obaied
Irina Roslyakova
With the increasing rate of newly developed materials, there is a noticeable increasing demand for accurate descriptions of thermophysical properties below the room temperature. Such demands make it essential to use modern data mining and machine learning techniques and provide a reliable description for the pure elements and compounds for low temperature ranges. A few recent attempts were made to model the thermophysical behavior below room temperature in the framework of the 3rd generation Calphad databases [1-4]. Nonetheless, accurate data for a large number of pure elements and the majority of known compounds are still unavailable to researchers. This is either due to the lack of data available for these models to fit, or because some of them are only valid for pure elements. The proposed modelling work uses an automated approach to produce a robust and reliable thermophysical descriptions for a very wide range of pure elements and compounds. Cp and S at room temperature are used as only input to fit a Debye temperature plus one mathematically simple correction term. The Debye model can then again be fitted by a polynomial expression to enable the use of simple polynomials down to zero Kelvin. This approach provides a straight-forward polynomial description for elements and compounds that can be universally utilized in different computational thermodynamic software. Moreover, they are more accurate than the solutions that rely on pure Einstein or pure Debye models.
