Band-gap regression with architecture-optimized message-passing neural
  networks
Tim Bechtel Daniel T. Speckhard Jonathan Godwin Claudia Draxl
Graph-based neural networks and, specifically, message-passing neural
networks (MPNNs) have shown great potential in predicting physical properties
of solids. In this work, we train an MPNN to first classify materials through
density functional theory data from the AFLOW database as being metallic or
semiconducting/insulating. We then perform a neural-architecture search to
explore the model architecture and hyperparameter space of MPNNs to predict the
band gaps of the materials identified as non-metals. The parameters in the
search include the number of message-passing steps, latent size, and
activation-function, among others. The top-performing models from the search
are pooled into an ensemble that significantly outperforms existing models from
the literature. Uncertainty quantification is evaluated with Monte-Carlo
Dropout and ensembling, with the ensemble method proving superior. The domain
of applicability of the ensemble model is analyzed with respect to the crystal
systems, the inclusion of a Hubbard parameter in the density functional
calculations, and the atomic species building up the materials.