Parameterization and Validation of Thermochemical Models of Glass by Advanced Statistical Analysis of Spectral Data

  • Jan Macháček
  • Mária Chromčíková
  • Marek LiškaEmail author
Part of the Hot Topics in Thermal Analysis and Calorimetry book series (HTTC, volume 11)


Shakhmatkin and Vedishcheva proposed the associated solutions’ thermodynamic model (SVTDM) of glasses and glass-forming melts. This model considers glasses and melts as an ideal solution formed from saltlike products of chemical reactions between the oxide components and the original (unreacted) oxides. The model does not use adjustable parameters; only the standard Gibbs energies of the formation of crystalline compounds and the analytical composition of the system considered are used as input parameters. A nonlinear regression treatment with the help of a genetic algorithm is used for the optimization of molar Gibbs energies by minimizing the sum of squares of deviations between experimental and calculated structure units’ distributions. In such a manner, the non-ideality of glass systems is reflected. The proposed method of using the optimized effective parameters (i.e., reaction Gibbs energies) within the SVTDM copes with most frequently met weak points of this method, i.e., missing of thermodynamic data for some components of SVTDM; missing of some components in the SVTDM because of insufficient knowledge of particular phase diagram or because of taking into account only the stable crystalline phases (and ignoring, e.g., the metastable ones); the assumption of zero mixing enthalpy connected with the supposed ideality of the studied glass system; the assumption of regular mixing entropy connected with the supposed ideality of the studied glass system; and the uncertainty in the mixing entropy originating in the uncertainty of molecular weight of individual components.


Gibbs Energy Chemical Equilibrium Molar Enthalpy Equilibrium Phase Diagram Standard Gibbs Energy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Slovak Grant Agency for Science under the grant VEGA 2/0088/16 and by the Slovak Research and Development Agency Project ID: APVV-0487-11. This work was also supported by the Grant Agency of the Czech Republic through the Grant No. P108/10/1631.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Jan Macháček
    • 1
  • Mária Chromčíková
    • 2
  • Marek Liška
    • 2
    Email author
  1. 1.University of Chemistry and TechnologyPragueCzech Republic
  2. 2.Joint Glass Center of IIC SAS, TnUAD, and FChPT STUVitrum LaugaricioTrenčínSlovakia

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