In recent years, following the increased availability of computational material databases , Machine Learning (ML) and data-driven approaches have opened new frontiers for accelerating materials discovery. These aim at overcoming the limitations imposed by the expensive physical simulations adopted in density functional theory (DFT), which allow only for a narrow exploration of the chemical space. Furthermore, DFT suffers from systematic errors due to numerical approximations occurring in any solver . Besides the computational advantages, ML models can also discover novel patterns that are otherwise hard to identify by only leveraging traditional chemical knowledge .