203 citations · 693 across the 29 of their papers we have counts for
6 papers · 1 filter
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…
Pushing thermal conductivity to its lower limit in crystals with simple structures
Zezhu Zeng, Xingchen Shen, Ruihuan Cheng +7
Materials with low thermal conductivity usually have complex crystal structures. Herein we experimentally find that a simple crystal structure material AgTlI2 (I4/mcm) owns an extr…