MOF tomography Some of the most successful applications of machine learning have been in computer vision. The primary reason for this is that while images contain a wealth of information, it is hard to extract good features that can then be fed into a model (e.g., training a linear regressor on a flat vector of all the pixel values will not work). Convolutional neural networks (CNNs) and related architectures are trainable feature extractors. That means, given some data they can learn to extract the most predictive features (thereby enabling the use of techniques such as transfer learning). However, it is not obvious how one can convert structures, which might have a varying number of atoms and which unit cells might be skewed, into "rectangular" image tensors of fixed size. Additionally, one also needs to consider that one would like to also encode the chemistry of a given material. In mofdscribe, we implement featurizers that voxelize approximately cubic supercells of crystal structures into 3D images (which one could then process using a 3D CNN). Also for these featurizers, we allow the users to use aggregations of custom properties (e.g., Pettifor scale, electronegativities, atomic numbers) as the value for the voxels instead of just binary indication of occupied/unoccupied. Of course, for example, for low-data applications, our approach can also only encode the geometry as a binary encoding, density, or using a truncated distance function. These features will, as initial results in the literature indicate, allow for the use of state-of-the-art computer vision models (including self-supervised pre-training and transfer learning) on reticular materials.