The chemical environment of each atom is described using a feature vector comprised of elemental properties as well as descriptors for chemical and geometric similarities. For the elemental properties, we include atomic number and mass, row and column on the periodic table, covalent radii, and Pauling electronegativity. To capture geometric and chemical similarities, we use AGNI fingerprint and Gaussian symmetry functions, in addition to the Voronoi tessellations based features on the statistics of atomic properties of neighbouring atoms. These features were computed for each atom of the crystals using Matminer and Pymatgen. We use gradient boosted decision trees (GBDT) machine learning model as implemented in XGBoost 50 to map the feature vectors to the heat capacity corresponding to each atom. To The final models, which are made available online, are train over all structures to exploit the full dataset.