7 papers
Crystal structure prediction with nuclear quantum and finite-temperature effects via deep free energy learning
Xiaoyang Wang, Yinan Wang, Wenbo Zhao +4
Accurate crystal structure prediction (CSP) requires accounting for finite-temperature and nuclear quantum effects, yet first-principles evaluation of the free energy surface (FES)…
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…
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…
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…
Neural Canonical Transformations for Quantum Anharmonic Solids of Lithium
Qi Zhang, Xiaoyang Wang, Rong Shi +3
Lithium is a typical quantum solid, characterized by cubic structures at ambient pressure. As the pressure increases, it forms more complex structures and undergoes a metal-to-semi…
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…