GNNs transform molecular graphs into node and edge representations, offering state-of-the-art performance for tasks related to materials . HDNNs, on the other hand, convert cartesian coordinates of atoms into environmental representations using predefined or learnable descriptors . Both GNN and HDNN models have shown significant success in quantum chemical property calculations . Each approach has its own advantages and disadvantages, and computational costs remain high, with a significant impact from the type of the training dataset. Electronic density of states (eDOS) is key quantity that describe the electronic properties of materials. Accurate prediction of eDOS is essential for understanding and designing new materials with desired electronic properties. The calculation of DOS with quantum chemical methods is computationally very expensive, thereby impeding high-throughput screening of compounds. Furthermore, the computation of DOS for compounds comprising thousands of independent atoms is a daunting task in quantum chemistry due to the scaling of the more accurate methods. Hence, the application of ML techniques offers significant advantages in addressing such computational challenges. Recently, GNNs have been used to predict the spectral properties of crystal structures . Fung et al used a GNN model to predict eDOSs of alloy surfaces and bulk crystal materials. To calculate the phDOS and eDOS of crystalline materials, Kong et al. adapted GATGNN and proposed the Mat2Spec model. Its predictions of ground-truth eDOS gave a mean absolute error (MAE) of 3.64 eV, the best result amongst various models. Euclidian Neural Networks have also been applied to predict phDOS based only on crystal structure . Mahmoud et al. proposed a sparse Gaussian process regression model to predict the electronic density of states (eDOS) of silicon structures.