Pinghui Mo

发表

Transfer Learning of Potential Energy Surfaces for Efficient Atomistic Modeling of Doping and Alloy

Pinghui Mo, Mengchao Shi, Wenze Yao, 2020, IEEE Electron Device Letters.

Efficient ab initio analysis of quantum confinement and band structure effects in ultra-scaled Si1−xGex gate-all-around and fin field-effect transistors for sub-10 nm technology nodes

Pinghui Mo, Jie Liu, Chuanxiang Tang, 2018, Journal of Computational Electronics.

Electrical-field-induced magnetic Skyrmion ground state in a two-dimensional chromium tri-iodide ferromagnetic monolayer

Pinghui Mo, Mengchao Shi, Jie Liu, 2018 .

Strain-enhanced electron mobility and mobility anisotropy in a two-dimensional vanadium diselenide monolayer

Pinghui Mo, Mengchao Shi, Jie Liu, 2019, Journal of Applied Physics.

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

Jie Liu, Pinghui Mo, Mengchao Shi, 2022, npj Computational Materials.

Deep Neural Network for Accurate and Efficient Atomistic Modeling of Phase Change Memory

Pinghui Mo, Mengchao Shi, Jie Liu, 2020, IEEE Electron Device Letters.

DeePMD-kit v2: A software package for deep potential models

Rhys E. A. Goodall, E. Weinan, D. York, 2023, The Journal of chemical physics.

Multi-scale analysis of strain-dependent magnetocrystalline anisotropy and strain-induced Villari and Nagaoka-Honda effects in a two-dimensional ferromagnetic chromium tri-iodide monolayer

Pinghui Mo, Mengchao Shi, Jie Liu, 2018, Journal of Applied Physics.

Efficient ab initio analysis of quantum confinement and band structure effects in ultra-scaled Si1−xGex gate-all-around and fin field-effect transistors for sub-10 nm technology nodes

Jie Liu, Pinghui Mo, Chuanxiang Tang, 2018, Journal of Computational Electronics.