With an increasing demand for high-performing and cost-effective catalysts, it became necessary to explore much wider chemical space, not limited to the current database, e.g., Materials Project (MP) and The Open Quantum Materials Database (OQMD). The development of computational catalysis field with increasing accuracy and efficiency of density functional theory (DFT) calculations and machine learning (ML) technologies has enabled an accelerated discovery of novel catalysts from wide varieties of elemental compositions and crystal structures . DFT and ML-based high-throughput virtual screening (HTVS) has indeed helped to evaluate catalytic activity and stability of materials rapidly and accurately from database , suggesting new candidate materials for various applications. This would have taken much more time and resources if the traditional brute force approach had been used. However, the current HTVS approach relies on the existing database although they only include a small portion of all possible materials in chemical space. This limitation could in part be overcome by expanding searching space through elemental substitution of the existing materials or genetic algorithm . The main bottleneck of these approaches, however, is that timeconsuming DFT calculations are required for a large number of candidate materials to evaluate their properties. For example, an elemental substitution approach could expand the chemical space by a hundred times, but it is practically impossible to perform geometry relaxations for all those structures. To tackle this challenge, various machine learning models have been reported, where crystal graph convolutional neural network (CGCNN) developed by Xie and Grossman being the most remarkable one. CGCNN represented crystal structures as multi-graphs consisting of edges and nodes using atomic distances and atom feature vectors, achieving high prediction accuracy for various bulk properties such as formation energies (0.039 eV/atom of mean absolute error (MAE)). Several other graph-based models have also been suggested for the prediction of other properties, e.g., binding energies, band gaps. , etc.