Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks.

ABSTRACT: In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial for the accurate prediction of defect properties. The performance of current graph neural network (GNN) models is limited due to the fact that defect properties depend strongly on the local atomic configurations near the defect sites and due to the oversmoothing problem of GNN. Herein, we demonstrate that persistent homology features, which encode the topological information on the local chemical environment around each atomic site, can characterize the structural information on defects. Using the dataset containing a wide spectrum of O-based perovskites with all available vacancies as an example, we show that incorporating the persistent homology features

SUBMITTER: Fang Z 

PROVIDER: S-EPMC11866744 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature