16 citations · 36 across the 9 of their papers we have counts for
5 papers · 1 filter
Linear-Scaling Quantum Transport from Machine-Learning Density Functional Theory Hamiltonians
Bang Liu, Yang Zhong, Zhi-Xin Guo +2
Quantum transport simulations that combine density functional theory (DFT) with the nonequilibrium Green's function formalism (DFT-NEGF) are important to modern technology, yet the…
First-Principles Electronic Structure Calculation of Crystals in Laboratory Magnetic Fields
Sichao Wang, Chengye Lü, Xingao Gong +2
External magnetic fields can qualitatively reshape the electronic structure of crystals, underpinning quantum Hall physics, Landau-level spectra and field-induced topological phase…
First-Principles Electron-Magnon Coupling with Machine-Learning Hamiltonians: From Band Renormalization to Transport
Shixu Liu, Xingding Li, Haozhe Li +4
In analogy to electron-phonon coupling (EPC), electron-magnon coupling (EMC) is expected to shape electronic structure, transport, and possibly unconventional superconductivity in…
Machine learning Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO
Zhenxing Dai, Zhong Yang, Mingjue Ni +4
Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations.…
Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians
Yang Zhong, Xiwen Li, Xingao Gong +1
Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern ph…