Modeling of binary molten-salt phase diagrams via neural networks

Computational neural networks are used as implicit thermodynamic models for calculating phase diagrams of binary molten salts. The neural network model accurately describes conformai ionic solution theory in binary systems thereby providing an efficient scheme for determining phase diagrams. Preliminary results are presented for model AC₂-BC₂ systems.