2 citations · 2 across the 2 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2026
GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP
Zihan Yan, Denan Li, Xin Wu +20
Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics proce…
cond-mat.mtrl-sci2026
A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials
Zihan Yan, Shengjie Tang, Yizhou Zhu
Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures.…
cond-mat.mtrl-sci2021★ 2 cited
Exploring low lattice thermal conductivity materials using chemical bonding principles
Jiangang He, Yi Xia, Wenwen Lin +4
Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and th…