5 papers
Nuclearity of Copper Clusters on hBN/SiC Heterostructure Modulates Molecular Adsorption
Reza Khakpour, Arsalan Hashemi, Xiaoya Chang +3
Defect engineering can transform inert two-dimensional (2D) materials into chemically active and electronically tunable platforms by creating anchoring sites for metal atoms and cl…
Dislocation-loop formation is a first-order phase transition
Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati +3
Dislocation loops are the elementary product of radiation damage in crystals, limiting reactor-component lifetimes, power-electronics reliability and the coherence of solid-state q…
Machine-learned prediction of carbon interstitial clusters in diamond
Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati +3
Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form…
TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials
Yong-Chao Wu, Xiaoya Chang, Tero Mäkinen +5
Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training s…
A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
Mingjie Wen, Jiahe Han, Wenjuan Li +3
The discovery and optimization of high-energy materials (HEMs) are constrained by the prohibitive computational expense and prolonged development cycles inherent in conventional ap…