Nonadiabatic coupled electron-nuclear dynamics upon electronic excitation underpin the microscopic mechanism and rational modulation of diverse photoinduced functional phenomena in materials, yet their direct first-principles simulations remain computationally demanding. Here we develop a framework for nonadiabatic exc...
Yunzhe Jia, Fan-Kai Xie, Yunfei Bai et al.· 0 citations
High-throughput experimentation and self-driving laboratories are drastically accelerating materials discovery, yet automated interpretation of X-ray powder diffraction (XRPD) data remains a critical rate-limiting step. Conventional search-match workflows rely heavily on expert manual intervention, while pure data-driv...
Hongqing V. Wang, Ming-Wei Chen, Hong Luo et al.· 0 citations
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 de...
Yi-Dong Huang, Teng-Long Lu, Hanwen Kang et al.· arXiv.org· 0 citations
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
This work releases OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms, providing a high-quality resource and robust ML potential for efficient simulations of sulfur- a...
