A natural alternative to this approach is making use of existing machine learning techniques, which have already seen success in several applications. The goal of ML approaches employed in the context of materials is to learn a function that maps a material to the desired attribute. DL techniques that can predict the underlying probability distribution of both structure and property and connect them are known as deep generative models. These models may extract prominent properties that define crystals by making use of trends in large datasets.