Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions.

ABSTRACT: Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.g., total energy, energy above the convex hull, energy band gap) and data-scarce mechanical properties (e.g., bulk and shear modulus). Our model incorporates four-body interactions, capturing periodicity and structural characteristics. It outperforms state-of-the-art models in 8 materials property regression tasks. Also, this model predicts local atomic environments and global struct

SUBMITTER: Madani M 

PROVIDER: S-EPMC11742729 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature