Deep-learning electronic-structure calculation of magnetic superstructures
Ab initio studies of magnetic superstructures are indispensable to research on emergent
quantum materials, but are currently bottlenecked by the formidable computational cost …
quantum materials, but are currently bottlenecked by the formidable computational cost …
Time-reversal equivariant neural network potential and Hamiltonian for magnetic materials
This work presents Time-reversal Equivariant Neural Network (TENN) framework. With
TENN, the time-reversal symmetry is considered in the equivariant neural network (ENN) …
TENN, the time-reversal symmetry is considered in the equivariant neural network (ENN) …
Transferable Machine Learning Approach for Predicting Electronic Structures of Charged Defects
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 density …
various electrical properties of materials. However, the high computational cost of density …
Accelerating the electronic-structure calculation of magnetic systems by equivariant neural networks
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 …
complex local magnetic arrangements, and many of the magnetic superlattices have been …
Accelerating the calculation of electron-phonon coupling by machine learning methods
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 …
phenomena, but the high computational cost of the EPC matrix hinders the theoretical …