Traditionally, these properties are evaluated with simulation techniques such as density functional theory (DFT). While known for its accuracy, DFT is computationally intensive, often requiring access to high performance computing (HPC) platforms and parallelization across several nodes. DFT calculations also tend to scale poorly with system size, with a usual scaling factor of N 3 where N is the number of atoms in the system. Due to the computational expense of DFT, alternatives such as machine learning have become more attractive in recent years.