shown in Figure . The experimental data covers a temperature range of 325 -500 °C at pressures of 10 -1000 atm and near-stoichiometric inlet composition. Equilibrium compositions calculated with the conventional stoichiometric method as described above match the literature data perfectly up to pressures of 100 atm. At higher pressure, increasing deviations between the training data calculated by Shomate equations and the experimental literature data emerge. The predictions by Shomate equations thus deviate from the measured ammonia synthesis equilibrium at pressures above 100 atm. Therefore, the neural network presented here is trained in two different parameter ranges as discussed above. That way, it can be tested for its ability to correctly predict the equilibrium composition for pressures up to 100 bar on the one hand, while it is also investigated whether the same neural network could predict the results of the Shomate equations in a much wider range of parameters. Those, however, exhibit some deviation to experimental data, whose origin remains unclear to the authors.