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Tunable Quantum Anomalous Hall Effects in Ferromagnetic van der Waals Heterostructures
Feng Xue, Yusheng Hou, Zhe Wang +5
The quantum anomalous Hall effect (QAHE) has unique advantages in topotronic applications, but it is still challenging to realize the QAHE with tunable magnetic and topological pro…
Deep-learning electronic-structure calculation of magnetic superstructures
He Li, Zechen Tang, Xiaoxun Gong +3
Ab initio study of magnetic superstructures (e.g., magnetic skyrmion) is indispensable to the research of novel materials but bottlenecked by its formidable computational cost. For…
General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
Xiaoxun Gong, He Li, Nianlong Zou +3
Combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating…
Molecular conformer search with low-energy latent space
Xiaomi Guo, Lincan Fang, Yong Xu +4
Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as…
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
Zun Wang, Chong Wang, Sibo Zhao +4
Molecular dynamics is a powerful simulation tool to explore material properties. Most of the realistic material systems are too large to be simulated with first-principles molecula…