2 citations · 2 across the 4 of their papers we have counts for
4 papers
Predicting large-supercell defect formation energies from machine-learning charge density models trained on small supercells
Junjie Zhou, Menglin Huang, Shiyou Chen
First-principles defect calculations are often limited by the cost of the large supercells required to suppress image interactions. Machine-learning interatomic potentials (MLIPs)…
Nonradiative Multiphonon Model of Deep-Level Transient Spectroscopy: Beyond Henry-Lang Model
Menglin Huang, Shanshan Wang, Junjie Zhou +2
Deep-level transient spectroscopy (DLTS) is a key experimental method for defect characterization, yet its analysis remains controversial, and the two widely used models developed…
"One defect, one potential" strategy for accurate machine learning prediction of defect phonons
Junjie Zhou, Xinpeng Li, Menglin Huang +1
Atomic vibrations play a critical role in phonon-assisted electron transitions at defects in solids. However, accurate phonon calculations in defect systems are often hindered by t…
Defect Phonon Renormalization during Nonradiative Multiphonon Transitions in Semiconductors
Junjie Zhou, Shanshan Wang, Menglin Huang +2
As a typical nonradiative multiphonon transition in semiconductors, carrier capture at defects is critical to the performance of semiconductor devices. Its transition rate is usual…