Exploration of the mechanical properties of carbon-incorporated amorphous silica using a ...

Summary

The article discusses the use of the Graph Neural Network (GNN) in the form of PreFerred Potential (PFP) for efficiently simulating material properties, which is beneficial in fields such as theoretical and computational chemistry. It highlights the GNN's ability to outperform density functional theory (DFT) in cost-effectiveness and compute properties across various elements with high accuracy, impacting computational chemistry and materials science.