To corroborate the capability of D-CGCNN to successfully discover stable materials by predicting formation energies of the initial structures, we evaluated its success ratio and discoverability . The success ratio is defined as the ratio of DFT-validated stable materials among ML-predicted stable materials, and the discoverability is the proportion of successfully discovered stable materials by ML among total dataset. For 2e-ORR dataset, 3,345 out of 7,383 were predicted to be stable with 𝐸 𝑓 𝑀𝐿 ≤ 0.00 eV/atom, and 2,505 were DFT-validated to be stable among them, achieving 72.6 % of success ratio (Figure ). There are total 3,208 DFTidentified stable materials, thus the discoverability of D-CGCNN is 78.0 % (Figure ). In the case of Wren and Roost, stability of materials is overestimated, resulting in high "false positive", high discoverability and low success ratio (Figure ), which could also be observed from parity plots (Figure and Figure ). On the contrary, CGCNN-HD tends to underestimate the stability (Figure ), resulting in low 'false positive', low discoverability and high success ratio.