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Predicting formation energies of crystals is a common but computationally expensive task. In this work, it is therefore investigated how a neural network can be used as a tool for predicting formation energies with less computational cost compared to conventional methods. The investigated model shows promising results in predicting formation energies, reaching below a mean absolute error of 0.05 eV/atom with less than 4000 training datapoints. The model also shows great transferability, being able to reach below an MAE of 0.1 eV/atom with less than 100 training points when transferring from a pre-trained model. A drawback of the model is however that it is relying on descriptions of the crystal structures that include interatomic distances. Since these are not always accurately known, it is investigated how inaccurate structure descriptions affect the performance of the model. The results show that the quality of the descriptions definitely worsen the accuracy. The less accurate descriptions can however be used to reduce the search space in the creation of phase diagrams, and the proposed workflow which combines conventional density functional theory and machine learning shows a reduction in time consumption of more than 50 \% compared to only using density functional theory for creating a ternary phase diagram.