The use of machine learning methods for accelerating the design of functional materials has emerged at the forefront of computational materials discovery and deep learning is playing a key role it its enablement. The story of machine learning is one of representations and its application in materials discovery is no different. In the last decades, there have been proposals to represent materials as either graphs , multi-dimensional diffraction patterns , or even text . Recent advancements on high-throughput experimentation and the widespread open collaboration of data has lead to increasingly more available materials databases to be harnessed paving the way for richer and more expressive machine-learned interatomic potentials aiming to accelerate or even deputise expensive calculations by achieving near-density functional theory level accuracy .