309 citations · 1.3k across the 28 of their papers we have counts for
28 papers
A Physical Response-and-Memory Model for Muon Optimization
Yinze Hu, Hongjun Xiang, Xingao Gong +1
Training large language models is costly. How low a loss the same compute can ultimately reach depends on how each step's gradient is converted into a weight update; the rule that…
Recent Advances in Unconventional Ferroelectrics and Multiferroics
Hongyu Yu, Junyi Ji, Wei Luo +2
Emerging ferroic materials may pave a new way to next-generation nanoelectronic and spintronic devices due to their interesting physical properties. Here, we systematically review…
A Universal Spin-Orbit-Coupled Hamiltonian Model for Accelerated Quantum Material Discovery
Yang Zhong, Rui Wang, Xingao Gong +1
The accurate modeling of spin-orbit coupling (SOC) effects in diverse complex systems remains a significant challenge due to the high computational demands of density functional th…
Revealing Higher-Order Topological Bulk-boundary Correspondence in Bismuth Crystal with Spin-helical Hinge State Loop and Proximity Superconductivity
D. M. Zhao, Y. Zhong, T. Yuan +7
Topological materials are typically characterized by gapless boundary states originated from nontrivial bulk band topology, known as topological bulk-boundary correspondence. Recen…
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
Zaizhou Xin, Yang Zhong, Xingao Gong +1
Accurately calculating energies and atomic forces with linear-scaling methods is a crucial approach to accelerating and improving molecular dynamics simulations. In this paper, we…
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
Yang Zhong, Hongyu Yu, Jihui Yang +3
While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persi…
Transferable Machine Learning Approach for Predicting Electronic Structures of Charged Defects
Yuxing Ma, Yang Zhong, Yu Hongyu +2
The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of de…
Accelerating the electronic-structure calculation of magnetic systems by equivariant neural networks
Yang Zhong, Binhua Zhang, Hongyu Yu +2
Complex spin-spin interactions in magnets can often lead to magnetic superlattices with complex local magnetic arrangements, and many of the magnetic superlattices have been found…
Accelerating the calculation of electron-phonon coupling by machine learning methods
Yang Zhong, Zhiguo Tao, Weibin Chu +2
Electron-phonon coupling (EPC) plays an important role in many fundamental physical phenomena, but the high computational cost of the EPC matrix hinders the theoretical research on…
Capturing long-range interaction with reciprocal space neural network
Hongyu Yu, Liangliang Hong, Shiyou Chen +2
Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dy…
General time-reversal equivariant neural network potential for magnetic materials
Hongyu Yu, Boyu Liu, Yang Zhong +5
This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassin…
Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
Yang Zhong, Hongyu Yu, Mao Su +2
Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices…
Spin-Dependent Graph Neural Network Potential for Magnetic Materials
Hongyu Yu, Yang Zhong, Liangliang Hong +4
The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potenti…
Edge-based Tensor prediction via graph neural networks
Yang Zhong, Hongyu Yu, Xingao Gong +1
Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become…
Complex Spin Hamiltonian Represented by Artificial Neural Network
Hongyu Yu, Changsong Xu, Feng Lou +4
The effective spin Hamiltonian method is widely adopted to simulate and understand the behavior of magnetism. However, the magnetic interactions of some systems, such as itinerant…
A parallel orbital-updating based plane-wave basis method for electronic structure calculations
Yan Pan, Xiaoying Dai, Stefano de Gironcoli +3
Motivated by the recently proposed parallel orbital-updating approach in real space method, we propose a parallel orbital-updating based plane-wave basis method for electronic stru…
Two-Dimensional Node-Line Semimetals in a Honeycomb-Kagome Lattice
J. L. Lu, W. Luo, X. Y. Li +4
Recently, the concept of topological insulators has been generalized to topological semimetals, including three-dimensional (3D) Weyl semimetals, 3D Dirac semimetals, and 3D node-l…
A Parallel Orbital-Updating Approach for Electronic Structure Calculations
Xiaoying Dai, Xingao Gong, Aihui Zhou +1
In this paper, we propose an orbital iteration based parallel approach for electronic structure calculations. This approach is based on our understanding of the single-particle equ…
First-principles Study On The Electronic And Optical Properties Of Cubic ABX3 Halide Perovskites
Li Lang, Ji-Hui Yang, Heng-Rui Liu +2
The electronic properties of ABX3 (A = Cs, CH3NH3, NH2CHNH2; B = Sn, Pb; X = Cl, Br, I) type compounds in the cubic phase are systematically studied using the first-principles calc…
Polarization Enhancement in Perovskite Superlattices by Oxygen Octahedral Tilts
X. Z. Lu, X. G. Gong, H. J. Xiang
Interface engineering in perovskite oxide superlattices has developed into a flourishing field, enabling not only further tuning of the exceptional properties, but also giving acce…
Adaptive Finite Element Approximations for Kohn-Sham Models
Huajie Chen, Xiaoying Dai, Xingao Gong +2
The Kohn-Sham equation is a powerful, widely used approach for computation of ground state electronic energies and densities in chemistry, materials science, biology, and nanoscien…
Towards Direct-Gap Silicon Phases by the Inverse Band Structure Design Approach
H. J. Xiang, Bing Huang, Erjun Kan +2
Diamond silicon (Si) is the leading material in current solar cell market. However, diamond Si is an indirect band gap semiconductor with a large energy difference (2.4 eV) between…
Strong Dzyaloshinskii-Moriya Interaction and Origin of Ferroelectricity in Cu2OSeO3
J. H. Yang, Z. L. Li, X. Z. Lu +4
By performing density functional calculations, we investigate the origin of the skyrmion state and ferroelectricity in Cu2OSeO3. We find that the Dzyaloshinskii-Moriya interactions…
Giant Ferroelectric Polarization of CaMn7O12 Induced by a Combined Effect of Dzyaloshinskii-Moriya Interaction and Exchange Striction
X. Z. Lu, M. -H. Whangbo, Shuai Dong +2
By extending our general spin-current model to non-centrosymmetric spin dimers and performing density functional calculations, we investigate the causes for the helical magnetic or…