1 citations · 1 across the 4 of their papers we have counts for
5 papers
Machine learning for the design and prediction of soft-magnetic electromagnetic shielding FeCo-based alloys in laser cladding
Luting Wang, Suiyuan Chen, Xiancheng Zhu +4
Electromagnetic shielding materials play a pivotal role in both aerospace applications and daily life. However, their design and manufacturing still face persistent challenges. Mac…
A Density-Matrix Framework for Electronic-Structure Analysis of Electrolytes for Lithium Batteries
Mingkang Liu, Huize Yu, Lei Shen
Electrolyte reactivity in lithium batteries is shaped by molecular functional groups, Li solvation and salt-anion participation. Conventional quantum chemistry is too computa…
Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning
Yifan Li, Arravind Subramanian, Xiaoqing Liu +3
A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these directly affect adsorption energies,…
Physics-integrated Neural Network for Quantum Transport Prediction of Field-effect Transistor
Xiuying Zhang, Linqiang Xu, Jing Lu +2
Quantum-mechanics-based transport simulation is of importance for the design of ultra-short channel field-effect transistors (FETs) with its capability of understanding the physica…
Interpretable learning of voltage for electrode design of multivalent metal-ion batteries
Xiuying Zhang, Jun Zhou, Jing Lu +1
Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data, such as the design of current commercial Li-ion b…