Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation

The development of new materials is a time-consuming
and resource-intensive
process. Deep learning has emerged as a promising approach to accelerate
this process. However, accurately predicting crystal structures using
deep learning remains a significant challenge due to the complex,
high-dimensional nature of atomic interactions and the scarcity of
comprehensive training data that captures the full diversity of possible
crystal configurations. This work developed a neural network model
based on a data set comprising thousands of crystallographic information
files from existing crystal structure databases. The model incorporates
a self-attention mechanism to enhance prediction accuracy by learning
and extracting both local and global features of three-dimensional
structures, treating the atoms in each crystal as point sets. This
approach enables effective semantic segmentation and accurate unit
cell prediction. Experimental results demonstrate that for unit cells
containing up to 500 atoms, the model achieves a structure prediction
accuracy of 89.78%.