  Materials property predictions have improved from advances in
machine-learning algorithms, delivering materials discoveries and novel
insights through data-driven models of structure-property relationships. Nearly
all available models rely on featurization of materials composition, however,
whether the exclusive use of structural knowledge in such models has the
capacity to make comparable predictions remains unknown. Here we employ a deep
neural network (DNN) model, deepKNet, to learn structure-property relationships
in crystalline materials without explicit chemical compositions, focusing on
classification of crystal systems, mechanical elasticity, electrical behavior,
and phase stability. The deepKNet model utilizes a three-dimensional (3D)
momentum space representation of structure from elastic X-ray scattering theory
and simultaneously exhibits rotation and permutation invariance. We find that
the spatial symmetry of the 3D point cloud, which reflects crystalline symmetry
operations, is more important than the point intensities contained within,
which correspond to various planar electron densities, for making a successful
metal-insulator classification. In contrast, the intensities are more important
for predicting bulk moduli. Phase stability, however, relies more upon chemical
composition information, where our structure-based model exhibits limited
predictability. We find learning the materials structural genome in the form of
a chemistry-agnostic DNN demonstrates that some crystal structures inherently
host high propensities for optimal materials properties, which enables the
decoupling of structure and composition for future co-design of
multifunctionality.
