Author

Hanwen Kang

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Preprint Jul 2026

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.

Yidong Huang, Tenglong Lu, Hanwen Kang et al. · 0 citations

Preprint Jul 2026

Are Machine Learning Interatomic Potentials Truly Practical? A Benchmark of 23 Mainstream Models

This work benchmarks 23 mainstream open-source MLIPs on a low-cost NVIDIA DGX Spark, using a fixed 192-atom system under a unified ASE-based pipeline, and evaluates three dimensions: predictive accuracy, MD simulation throughput, and atomic scalability.

Hanwen Kang, Tenglong Lu, Sheng Meng et al. · 0 citations