Junfeng Huang

Aligning Heterogeneous DFT Datasets: A Graph Neural Network Approach to Cross-Functional Formation Energies

A structure-aware graph neural network is trained to predict cross-functional energy residuals and align inconsistent DFT energy scales, which enables reliable predictions of phase stability, battery voltage profiles, and reaction thermodynamics, while allowing the integration of multi-source DFT data to advance the development of high-performance materials foundation models.