11 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…
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{…
Symmetry Adapted Analysis of Screw Dislocation: Electronic Structure and Carrier Recombination Mechanisms in GaN
Yuncheng Xie, Haozhe Shi, Menglin Huang +3
As fundamental one-dimensional defects, screw dislocations profoundly reshape the energy landscape and carrier dynamics of crystalline materials. By restoring the exact algebra of…
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.…