In periodically driven open quantum systems, nonnormality renders the Floquet spectrum insufficient as the system approaches the thermodynamic limit, so that pseudospectra are needed to characterize the dynamics accurately. While conventional approaches mainly focus on the local dynamics of isolated pseudospectra, thei...
Yun-Cheng Xie, Hao-Zhe Shi, Zhuo-Cheng Ma et al.· 0 citations
External magnetic fields, through their coupling to orbital and spin motion, complicate the correlated electronic states and impose coordinate-dependent phases on the wavefunction, thereby making accurate electronic structure calculations substantially more demanding. Recently, neural network-based quantum Monte Carlo...
Cheng-Ye Lu, Wei-Zhong Fu, Xin-Gao Gong et al.· 0 citations
External magnetic fields can qualitatively reshape the electronic structure of crystals, underpinning quantum Hall physics, Landau-level spectra and field-induced topological phases. Their first-principles treatment at laboratory-scale fields is, however, hindered by magnetic-flux quantization, which requires magnetic...
Si-Chao Wang, Cheng-Ye Lu, Xin-Gao Gong et al.· 0 citations
Quantum transport simulations that combine density functional theory (DFT) with the nonequilibrium Green's function formalism (DFT-NEGF) are important to modern technology, yet their unfavorable scaling has long confined predictive simulations to small, idealized systems far below the ten-thousand-atom scale of realist...
Bang Liu, Yang Zhong, Zhi-Xin Guo et al.· 0 citations
On-the-fly N${^2}$AMD (Neural network NAMD), a machine learning framework that makes on-the-fly NAMD in solids a reality, by employing an equivariant neural network to predict the system Hamiltonian, delivers excited-state energies, forces, and non-adiabatic coupling vectors at a fraction of the cost of ab initio calcu...
Changwei Zhang, Yang Zhong, Zhi-Guo Tao et al.· Physical Review Letters· 1 citation
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