617 citations · 1.8k across the 38 of their papers we have counts for
38 papers
Small-supercell and Small-dataset Training Strategy of Machine Learning Interatomic Potentials for Point Defects
Zhenxing Dai, Mingjue Ni, Xinpeng Li +3
Machine learning interatomic potentials (MLIPs) can treat large-scale material systems with near first-principles accuracy and have been widely used to accelerate point-defect simu…
Carrier Capture at Defects from Finite-Temperature Lattice Dynamics
Menglin Huang, Shanshan Wang, Shiyou Chen
Defect-assisted carrier capture is commonly described within nonradiative multiphonon (NMP) theory using normal modes of the equilibrium defect structure. This description becomes…
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…
A wrong ground-state structure of HfO predicted by machine-learning interatomic potentials based on the PBE functional
Shuqi Tang, Jinchen Wei, Kang Wang +4
Machine-learning interatomic potentials (MLIPs) have become powerful tools for material simulations. Many MLIPs are trained based on density functional theory (DFT) datasets genera…
Substitutional platinum as an efficient nonradiative recombination center in silicon
Zhenxing Dai, Menglin Huang, Xin-Gao Gong +1
Platinum (Pt) is widely used for carrier-lifetime control in silicon power devices, yet the microscopic nonradiative recombination mechanism of the substitutional platinum ($\text{…