146 citations · 147 across the 4 of their papers we have counts for
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Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates
Jing Wu, E Zhou, An Huang +3
High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1)…
High-throughput discovery of novel cubic crystal materials using deep generative neural networks
Yong Zhao, Mohammed Al-Fahdi, Ming Hu +4
High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quan…
Activated lone-pair electrons lead to low lattice thermal conductivity: a case study of boron arsenide
Guangzhao Qin, Zhenzhen Qin, Huimin Wang +1
Reducing thermal conductivity () is an efficient way to boost the thermoelectric performance to achieve direct solid-state conversion to electrical power from thermal energy, wh…
A nonlinear hyperelasticity model for single layer blue phosphorus based on ab-initio calculations
Reza Ghaffari, Farzad Shirazian, Ming Hu +1
A new hyperelastic membrane material model is proposed for single layer blue phosphorus (), also known as blue phosphorene. The model is fully nonlinear and captures th…