Spin DOS featurizer consists of four convolutional blocks which contain two residual convolutional block attention module (CBAM) blocks (Fig. ) , intended to learn features effectively by focusing on important features (Supplementary Fig. ) . The residual connection prevents loss of extracted features, which could occur due to the depth of models . After the subsequent convolutional blocks, total DOS feature maps of each spin state are obtained at the end of the featurizers, where they are concatenated and served as an input for total DOS featurizer. The total DOS featurizer aggregates the integrated feature maps of two spin states to form comprehensive feature maps covering all spin states and orbitals, which are then flattened and concatenated with the component vector. Finally, fully connected neural network (FCNN) is applied to predict formation energies. The key aspects of ESNet are its optimal operation for aggregation of partially projected DOS and chemically inspired architecture considering spin states and orbitals separately.