To train ESNet, we collected DFT-calculated DOS from Materials Project (MP) database . In total, MP includes 62,842 materials, of which all orbitals have at least one spin state per energy for absolute energy range from -20 to 10 eV. We note that the dataset used in this work consist of only s, p and d-block elements, but it is possible to include f-block elements in training/test dataset by integrating extra channels in the model. Additionally, we highlight that our model could be universally used for formation energy prediction as it was trained with the dataset consisting of diverse compositions and crystal structures (Supplementary Fig. and Fig. ). We evaluated the prediction performance of ESNet and compared with other ML models based on different input features and algorithms (Fig. ). Although the focus of this work is IS2RE performance, we also discuss RS2RE results, which could be used in structureto-energy and force (S2EF) mapping in the future We also compared ESNet with other models (kernel ridge regressor (KRR) , support vector regressor (SVR) , random forest regressor (RFR) , XGBoost 36 , deep neural network (DNN) with two fully connected layers ) using extracted DOS features and composition information as inputs (Fig. -i and Supplementary Table ). DOS feature-based models demonstrated higher prediction accuracy than DOS-free feature-based models, demonstrating the suitability of DOS as inputs for IS2RE. Interestingly, DOS-signal based models (ESNet and CNN) showed even higher accuracy, where ESNet particularly outperformed all ML models for both IS2RE and RS2RE (Supplementary Fig. ). A comparison between DOS featurebased models and full DOS signal-based convolutional neural network models (ESNet, CNN ) demonstrates that only a few representative features are insufficient to properly describe properties of solid materials (Fig. and). We further mention that ESNet outperformed the conventional CNN, highlighting the importance of chemically inspired architecture and autonomously learned weights. Given the best prediction accuracy of ESNet for IS2RE, we benchmarked its screening performance. Practically, ML models are employed in HTVS to determine whether new candidate materials are stable or not, thus classification metrics can be used to benchmark the screening performance .