  The success of machine learning (ML) in materials property prediction depends
heavily on how the materials are represented for learning. Two dominant
families of material descriptors exist, one that encodes crystal structure in
the representation and the other that only uses stoichiometric information with
the hope of discovering new materials. Graph neural networks (GNNs) in
particular have excelled in predicting material properties within chemical
accuracy. However, current GNNs are limited to only one of the above two
avenues owing to the little overlap between respective material
representations. Here, we introduce a new concept of formula graph which
unifies stoichiometry-only and structure-based material descriptors. We further
develop a self-attention integrated GNN that assimilates a formula graph and
show that the proposed architecture produces material embeddings transferable
between the two domains. Our model can outperform some previously proposed
structure-agnostic models and their structure-based counterparts while
exhibiting better sample efficiency and faster convergence. Finally, the model
is applied in a challenging exemplar to predict the complex dielectric function
of materials and nominate new substances that potentially exhibit
epsilon-near-zero phenomena.
