Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive
thermodynamic properties
is needed to evaluate material performance and permit high-throughput
materials modeling for a diverse array of technology applications.
To alleviate the prohibitive computational expense of high-throughput
configurational sampling with density functional theory (DFT), surrogate
modeling strategies like cluster expansion are many orders of magnitude
more efficient but can be difficult to construct in systems with high
compositional complexity. We therefore employ minimal-complexity graph
neural network models that accurately predict and can even extrapolate
to out-of-train distribution formation energies of DFT-relaxed structures
from an ideal (unrelaxed) crystallographic representation. This enables
the large-scale sampling necessary for various thermodynamic property
predictions that may otherwise be intractable and can be achieved
with small training data sets. Two exemplars, optimizing the thermodynamic
stability of low-density high-entropy alloys and modulating the plateau
pressure of hydrogen in metal alloys, demonstrate the power of this
approach, which can be extended to a variety of materials discovery
and modeling problems.