With increasing demands toward high-performing and cost-effective materials, it is becoming important to develop new strategies to accelerate an exploration of broad chemical space. The conventional approaches have relied on database corresponding to very small portion of chemical space, and time-consuming ab-initio calculations requiring huge computing resources . Recently, various strategies to expand chemical space were developed, and machine learning (ML) has been utilized as powerful and efficient tools to facilitate materials discovery by reducing the number of required DFT calculations. In this aspect, fast and accurate ML models are desirable to enable high-throughput virtual screening (HTVS) of tens of thousands of materials .