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Machine learning (ML) is becoming increasingly popular for predictingmaterial properties to accelerate materials discovery. Because materialproperties are strongly affected by its crystal structure, a key issue isconverting the crystal structure into the features for input to the ML model.Currently, the most common method is to convert the crystal structure into agraph and predicting its properties using a graph neural network (GNN). SomeGNN models, such as crystal graph convolutional neural network (CGCNN) andatomistic line graph neural network (ALIGNN), have achieved highly accuratepredictions of material properties. Despite these successes, using a graph torepresent a crystal structure has the notable limitation of losing the crystalstructure's three-dimensional (3D) information. In this work, we proposeDeepCrysTet, a novel deep learning approach for predicting material properties,which uses crystal structures represented as a 3D tetrahedral mesh generated byDelaunay tetrahedralization. DeepCrysTet provides a useful framework thatincludes a 3D mesh generation method, mesh-based feature design, and neuralnetwork design. The experimental results using the Materials Project datasetshow that DeepCrysTet significantly outperforms existing GNN models inclassifying crystal structures and achieves state-of-the-art performance inpredicting elastic properties.

DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials