Abstract We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. test several methods that map atomic and a defect onto sparse parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them our further investigations. demonstrate accuracy parameterizations range important properties such as ban...