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Use the Crystal Graph Convolutional Neural Network (CGCNN) to make predictions for materials properties. The CGCNN model has been trained using data taken from Materials Project and the public version of this database. Currently independently predicted properties are the bulk modulus, shear modulus, C11, C12 and C44. Using the bulk and shear moduli, the Young's modulus and Poisson's ratio are derived.