Our featurization includes the atom identity and descriptors to capture both chemical and geometric similarities of the local atomic environments (Figure ). To capture the local chemistry, we use Voronoi-tessellation based descriptors, which give statistics of chemical heuristics for the neighboring atoms. Furthermore, we use symmetry functions and AGNI fingerprints to encode geometric similarities. These descriptors are expressive enough to provide enough flexibility for the machine learning model to capture the similarities between the chemical environments to predict heat capacity.