1 citations · 1 across the 8 of their papers we have counts for
9 papers
Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Yunzhe Jia, Fankai Xie, Yunfei Bai +3
Nonadiabatic coupled electron-nuclear dynamics upon electronic excitation underpin the microscopic mechanism and rational modulation of diverse photoinduced functional phenomena in…
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…
MatDiffract: A Material-Informed Automated Analysis Platform for X-ray Powder Diffraction
Hongqing Wang, Mingwei Chen, Hongjie Luo +5
High-throughput experimentation and self-driving laboratories are drastically accelerating materials discovery, yet automated interpretation of X-ray powder diffraction (XRPD) data…
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…
Collective phase modes in twisted -wave superconducting bilayers
Yin Shi, Mengxian Zhao, Fei Yang +2
Twisted cuprate bilayers have been predicted to host high-temperature chiral superconductivity, originating from higher-order Josephson coupling processes. In such two-dime…
Quench of chiral superconductivity by quantum phase fluctuations in twisted cuprate bilayers
Yin Shi, Mengxian Zhao, Fei Yang +2
Following theoretical proposals of chiral superconductivity in twisted cuprate bilayers, experimental signatures of time-reversal symmetry breaking (TRSB) remain highly con…