1 citations · 1 across the 2 of their papers we have counts for
4 papers
High-Throughput GW Calculations via Machine Learning
Ragab. A. Abdelghany, Chih-En Hsu, Hung-Chung Hsueh +2
We present a machine learning (ML) framework that predicts quasiparticle energies across molecular dynamics (MD) trajectories with high accuracy and efficiency. Using only…
Deep learning of topological phase transitions from entanglement aspects: An unsupervised way
Yuan-Hong Tsai, Kuo-Feng Chiu, Yong-Cheng Lai +5
Machine learning techniques have been shown to be effective to recognize different phases of matter and produce phase diagrams in the parameter space interested, while they usually…
Deep learning of topological phase transitions from entanglement aspects for two-dimensional chiral p-wave superconductors
Ming-Chiang Chung, Tsung-Pao Cheng, Guang-Yu Huang +1
Applying deep learning to investigate topological phase transitions (TPTs) becomes a useful method due to not only its ability to recognize patterns but also its statistical excell…
Deep learning of topological phase transitions from entanglement aspects
Yuan-Hong Tsai, Meng-Zhe Yu, Yu-Hao Hsu +1
The one-dimensional -wave superconductor proposed by Kitaev has long been a classic example for understanding topological phase transitions through various methods, such as exam…