Using ESNet trained with 62,842 data from MP database, we performed HTVS to discover stable materials. Note that the optimal thresholds of Ef ML and Ehull ML determined for ESNet were used to maximize F1-score. We applied ESNet to 2e-ORR dataset to discover stable unrelaxed structures, where more details on the dataset can be found in the method section. Out of 22,709 total bulk structures, 7,168 structures were relaxed and used for validation of ML models, thus formation energies of 15,318 unrelaxed structures were predicted (Fig. ). To visualize the screening process and distribution of Ef ML of 2e-ORR dataset, we used uniform manifold approximation and projection (UMAP) which reduces the dimension of input DOS into two dimensions (Fig. ). Initially, 7,710 structures satisfied Ef ML ≤ -0.02 eV/atom, and only 2,742 structures additionally satisfied Ehull ML ≤ 0.13 eV/atom. Thus, negative Ehull ML indicates that materials are predicted to be more stable than the existing most stable material in MP database. Finally, DFT relaxations were performed to validate 2,742 materials, where 926 materials satisfied both Ef DFT ≤ 0 eV/atom and Ehull DFT ≤ 0.1 eV/atom. This result demonstrates that ESNet found 926 stable and synthesizable materials by performing only 18 % of DFT calculations.