15 citations · 16 across the 3 of their papers we have counts for
3 papers
physics.comp-ph2024★ 1 cited
Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks
Rui Wang, Hongyu Yu, Yang Zhong +1
The application of machine learning methodologies for predicting properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnol…
physics.comp-ph2023
Transferable Machine Learning Approach for Predicting Electronic Structures of Charged Defects
Yuxing Ma, Yang Zhong, Yu Hongyu +2
The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of de…
cond-mat.str-el2022★ 15 cited
Electronically phase separated nano-network in antiferromagnetic insulating LaMnO3/PrMnO3/CaMnO3 tricolor superlattice
Qiang Li, Tian Miao, Huimin Zhang +15
Strongly correlated materials often exhibit an electronic phase separation (EPS) phenomena whose domain pattern is random in nature. The ability to control the spatial arrangement…