We introduce a machine learning model for predicting the electronic properties of crystal structures. By encoding crystals through the pair distribution function and atomic types embedding layer, we capture the chemical information of crystals which is then stored in a machine-learning database. Used on the original Mat2Spec dataset, we find our model to be very competitive with the original Mat2Spec model. Trained on the MP2022 dataset, we can predict additional scalar quantities. Predicting Fermi level energy or band gap and normalized eDOS values within the same model achieves MAE values comparable to deep graph neural networks.