58 citations · 169 across the 26 of their papers we have counts for
15 papers · 1 filter
Magnetorotons in Moiré Fractional Chern Insulators
Xiaoyang Shen, Chonghao Wang, Xiaodong Hu +5
The discovery of fractional Chern insulators (FCIs) unlocks exciting opportunities to explore emergent physical excitations arising from topological and geometric effects in novel…
Deep Band Crossings Enhanced Nonlinear Optical Effects
Nianlong Zou, He Li, Meng Ye +8
Nonlinear optical (NLO) effects in materials with band crossings have attracted significant research interests due to the divergent band geometric quantities around these crossings…
Fractional Chern insulators in moiré flat bands with high Chern numbers
Chonghao Wang, Xiaoyang Shen, Ruiping Guo +3
Recent discoveries of zero-field fractional Chern insulators in moiré materials have attracted intensive research interests. However, most current theoretical and experimental atte…
Stable magic angle in twisted Kane-Mele materials
Cheng Xu, Yong Xu, Wenhui Duan +1
We propose that flat bands and van Hove singularities near the magic angle can be stabilized against angle disorder in the twisted Kane-Mele model. With continuum model and maximal…
Deep learning density functional theory Hamiltonian in real space
Zilong Yuan, Zechen Tang, Honggeng Tao +11
Deep learning electronic structures from ab initio calculations holds great potential to revolutionize computational materials studies. While existing methods proved success in dee…
Universal materials model of deep-learning density functional theory Hamiltonian
Yuxiang Wang, Yang Li, Zechen Tang +14
Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challeng…
Stabilizing fractional Chern insulators via exchange interaction in moiré systems
Xiaoyang Shen, Chonghao Wang, Ruiping Guo +3
Recent experimental discovery of fractional Chern insulator in moiré Chern band in twisted transition metal dichalocogenide homobilayers has sparked intensive interest in exploring…
Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials
Ting Bao, Runzhang Xu, He Li +5
Moiré-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such…
Giant and controllable nonlinear magneto-optical effects in two-dimensional magnets
Dezhao Wu, Meng Ye, Haowei Chen +2
The interplay of polarization and magnetism in materials with light can create rich nonlinear magneto-optical (NLMO) effects, and the recent discovery of two-dimensional (2D) van d…
Neural-network Density Functional Theory Based on Variational Energy Minimization
Yang Li, Zechen Tang, Zezhou Chen +7
Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current…
Valley-dependent Multiple Quantum States and Topological Transitions in Germanene-based Ferromagnetic van der Waals Heterostructures
Feng Xue, Jiaheng Li, Yizhou Liu +3
Topological and valleytronic materials are promising for spintronic and quantum applications due to their unique properties. Using first principles calculations, we demonstrate tha…
Equivariant Neural Network Force Fields for Magnetic Materials
Zilong Yuan, Zhiming Xu, He Li +6
Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is stil…
Deep-learning density functional perturbation theory
He Li, Zechen Tang, Jingheng Fu +5
Calculating perturbation response properties of materials from first principles provides a vital link between theory and experiment, but is bottlenecked by the high computational c…
DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
Yuxiang Wang, He Li, Zechen Tang +6
Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architec…
Enhancement of Ising superconductivity in monolayer NbSe via surface fluorination
Jizheng Wu, Wujun Shi, Chong Wang +3
Recently discovered Ising superconductors have garnered considerable interest due to their anomalously large in-plane upper critical fields (). However, the requisite stron…