6 papers
Aligning Heterogeneous DFT Datasets: A Graph Neural Network Approach to Cross-Functional Formation Energies
Yidong Huang, Tenglong Lu, Hanwen Kang +3
Heterogeneous density functional theory (DFT) calculations, particularly plane-wave implementations, introduce systematic formation energy errors ranging from tens to hundreds of m…
Graph Neural Network Force Fields (GPTFF-mol) for Organic Molecules from Optimization Trajectories (OpenGEM26)
Yifan Huang, Fankai Xie, Jiangnan Zheng +3
Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accurac…
Are Machine Learning Interatomic Potentials Truly Practical? A Benchmark of 23 Mainstream Models
Hanwen Kang, Tenglong Lu, Sheng Meng +1
Most MLIP benchmarks reward static accuracy while ignoring inference efficiency and hardware scalability -- driving model bloat with unclear real-world value. We benchmark 23 mains…
FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi +3
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy s…
Imaging the Meissner effect in pressurized bilayer nickelate with integrated multi-parameter quantum sensor
Junyan Wen, Yue Xu, Gang Wang +14
Recent reports on the signatures of high-temperature superconductivity with a critical temperature Tc close to 80 K have triggered great research interest and extensive follow-up s…
Superconductivity up to 17 K in the high-pressure rhombohedral-I phase of ReO3: a potential oxide analogy of hydride superconductors
P. F. Shan, T. L. Lu, Z. Y. Liu +10
As an A-site-vacant perovskite-type oxide, ReO3 undergoes sequential pressure-driven structural transitions associated with the rotation of ReO6 octahedra. The rhombohedral-I phase…