A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

This repository contains supporting material to generate datasets, train neural networks, and analyze their predictions on chemical equilibria. Four chemical systems are examined: Cement_system (i)_mech_mix: pure mechanical mixture (ii)_SS : two-component (Ba,Ra)SO4 solid solution model (iii)_SS_Sr: three-component (Sr,Ba,Ra)SO4 solid solution model The GEMS folder provides the GEM-Selektor input files for each chemical system, as well as the Python scripts that create the datasets and the conda environment. The resulting NumPy arrays are stored in Datasets. Two sample training scripts for KAN and MLP are available in the Training folder. The trained neural network models are saved in Models. Finally, the Analysis folder contains the predictions on the test set associated with each model and two Jupyter Notebooks to generate the error plots.