6 papers
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…
Efficient E(3)-equivariant framework for universal charge density prediction
Xiwen Li, Zaizhou Xin, Hongyu Yu +3
Electronic structure is ubiquitously obtained via density functional theory (DFT), where the charge density plays a central role. This work presents EdenGNN (Equivariant Density Gr…
General First-Principles Approach to Crystals in Finite Magnetic Fields
Chengye Lü, Yingwei Chen, Yuzhi Wang +5
We introduce a general first-principles methodology for computing electronic structure in a finite uniform magnetic field which allows for an arbitrary rational magnetic flux and n…
Intrinsic breakdown strength: theoretical derivation and first-principles calculations
Shixu Liu, Hongjun Xiang, Xin-Gao Gong +1
Intrinsic breakdown strength (F_bd), as the theoretical upper limit of electric field strength that a material can sustain, plays important roles in determining dielectric and safe…