  Materials representation plays a key role in machine learning based
prediction of materials properties and new materials discovery. Currently both
graph and 3D voxel representation methods are based on the heterogeneous
elements of the crystal structures. Here, we propose to use electronic charge
density (ECD) as a generic unified 3D descriptor for materials property
prediction with the advantage of possessing close relation with the physical
and chemical properties of materials. We developed an ECD based 3D
convolutional neural networks (CNNs) for predicting elastic properties of
materials, in which CNNs can learn effective hierarchical features with
multiple convolving and pooling operations. Extensive benchmark experiments
over 2,170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based
CNNs can achieve good performance for elasticity prediction. Especially, our
CNN models based on the fusion of elemental Magpie features and ECD descriptors
achieved the best 5-fold cross-validation performance. More importantly, we
showed that our ECD based CNN models can achieve significantly better
extrapolation performance when evaluated over non-redundant datasets where
there are few neighbor training samples around test samples. As additional
validation, we evaluated the predictive performance of our models on 329
materials of space group Fm-3m by comparing to DFT calculated values, which
shows better prediction power of our model for bulk modulus than shear modulus.
Due to the unified representation power of ECD, it is expected that our ECD
based CNN approach can also be applied to predict other physical and chemical
properties of crystalline materials.
