3 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential
Enze Hou, Xiaoyang Wang, Han Wang
Lead (Pb) is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses. Under shock-wave loading, its dynamic mechanical…
OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure
Yinan Wang, Xiaoyang Wang, Zhenyu Wang +3
High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. Yet, most large atomistic models are tr…
A Deep Learning Potential for Accurate Shock Response Simulations in Tin
Yixin Chen, Xiaoyang Wang, Wanghui Li +2
Tin (Sn) plays a crucial role in studying the dynamic mechanical responses of ductile metals under shock loading. Atomistic simulations serves to unveil the nano-scale mechanisms f…
Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning
Xiaoyang Wang, Chengqian Zhang, Zhenyu Wang +5
The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast ch…
ABACUS: An Electronic Structure Analysis Package for the AI Era
Weiqing Zhou, Daye Zheng, Qianrui Liu +55
ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It…