In this work, we report Electronic Structure Network (ESNet) based on convolutional neural network and convolution block attention modules (CBAM) using density of states (DOS) of unrelaxed initial structures as input representations. Initially, we compared DOS of unrelaxed initial structures (IS) and relaxed structures (RS) to validate the use of IS-DOS for crystal structure representations, where we observed high similarity between IS-DOS and RS-DOS. By comparing with other models, we found that CBAM in ESNet successfully captures the contribution of DOS signal, representing each spin and orbital of crystal, to formation energy and ESNet is ideally suited to IS2RE task with the highest accuracy and efficiency. Furthermore, ESNet achieved both high success ratio and discoverability, breaking an inverse correlation between them . The highest F1-score of ESNet suggested that one can practically use this model for materials discovery via HTVS along with proper chemical space expansion approaches .