  Machine learning (ML) is becoming increasingly popular for predicting
material properties to accelerate materials discovery. Because material
properties are strongly affected by its crystal structure, a key issue is
converting the crystal structure into the features for input to the ML model.
Currently, the most common method is to convert the crystal structure into a
graph and predicting its properties using a graph neural network (GNN). Some
GNN models, such as crystal graph convolutional neural network (CGCNN) and
atomistic line graph neural network (ALIGNN), have achieved highly accurate
predictions of material properties. Despite these successes, using a graph to
represent a crystal structure has the notable limitation of losing the crystal
structure's three-dimensional (3D) information. In this work, we propose
DeepCrysTet, a novel deep learning approach for predicting material properties,
which uses crystal structures represented as a 3D tetrahedral mesh generated by
Delaunay tetrahedralization. DeepCrysTet provides a useful framework that
includes a 3D mesh generation method, mesh-based feature design, and neural
network design. The experimental results using the Materials Project dataset
show that DeepCrysTet significantly outperforms existing GNN models in
classifying crystal structures and achieves state-of-the-art performance in
predicting elastic properties.
