most citedIntegrating Machine Learning with Triboelectric Nanogenerators: Optimizing Electrode Materials and Doping Strategies for Intelligent Energy Harves

16 citations · 53 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2025★ 4 cited

Synergistic effects of rare-earth doping on the magnetic properties of orthochromates: A machine learning approach

Guanping Xu, Zirui Zhao, Muqing Su +1

Multiferroic materials, particularly rare-earth orthochromates (RECrO), have garnered significant interest due to their unique magnetic and electric-polar properties, making th…

cond-mat.mtrl-sci2025★ 16 cited

Integrating Machine Learning with Triboelectric Nanogenerators: Optimizing Electrode Materials and Doping Strategies for Intelligent Energy Harves

Guanping Xu, Zirui Zhao, Zhong Lin Wang +1

The integration of machine learning techniques with triboelectric nanogenerators (TENGs) offers a transformative pathway for optimizing energy harvesting technologies. In this stud…

physics.comp-ph2025

Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational study

Zirui Zhao, Junchao Xia, Si Wu +5

In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low c…

cond-mat.mtrl-sci2024★ 11 cited

Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance

Zirui Zhao, Xiaoke Wang, Si Wu +5

We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior exp…

cond-mat.mtrl-sci2024★ 11 cited

Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials

Zirui Zhao, Hai-Feng Li

Understanding and predicting interface diffusion phenomena in materials is crucial for various industrial applications, including semiconductor manufacturing, battery technology, a…

cond-mat.mtrl-sci2024★ 11 cited

Predicting doping strategies for ternary nickel-cobalt-manganese cathode materials to enhance battery performance using graph neural networks

Zirui Zhao, Dong Luo, Shuxing Wu +3

The exceptional electrochemical performance of lithium-ion batteries has spurred considerable interest in advanced battery technologies, particularly those utilizing ternary nickel…