526 citations
- Chinese Academy of SciencesCN36 papers
- Aalto UniversityFI34 papers
- Institute of Theoretical PhysicsCN23 papers
- Institute of High Energy PhysicsCN17 papers
- Loughborough UniversityGB17 papers
- Tel Aviv UniversityIL17 papers
- Chinese University of Hong KongHK15 papers
- Yangzhou UniversityCN15 papers
- University of Science and Technology BeijingCN12 papers
- Harbin Institute of TechnologyCN8 papers
- University of Chinese Academy of SciencesCN8 papers
- George Washington UniversityUS7 papers
7 papers · 2 filters
Dissimilar thermal transport properties in -GaO and -GaO revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations
Xiaonan Wang, Jinfeng Yang, Penghua Ying +3
The lattice thermal conductivity (LTC) of GaO is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neur…
General-purpose machine-learned potential for 16 elemental metals and their alloys
Keke Song, Rui Zhao, Jiahui Liu +25
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…
Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
Ting Liang, Penghua Ying, Ke Xu +4
Amorphous silica (a-SiO) is a foundational disordered material for which the thermal transport properties are important for various applications. To accurately model the intera…
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
Zheyong Fan, Yang Xiao, Yanzhou Wang +3
We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroe…
Combining the D3 dispersion correction with the neuroevolution machine-learned potential
Penghua Ying, Zheyong Fan
Machine-learned potentials (MLPs) have become a popular approach of modelling interatomic interactions in atomistic simulations, but to keep the computational cost under control, a…
Sub-micrometer phonon mean free paths in metal-organic frameworks revealed by machine-learning molecular dynamics simulations
Penghua Ying, Ting Liang, Ke Xu +4
Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability and hold great potential in various applications, many of which requirin…