1 citations · 2 across the 7 of their papers we have counts for
7 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…
Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification
Ziduo Yang, Yi-Ming Zhao, Xian Wang +6
In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent di…
Local environment-based machine learning for molecular adsorption energy prediction
Yifan Li, Yihan Wu, Yuhang Han +4
Most machine learning (ML) models in Materials Science are developed by global geometric features, often falling short in describing localized characteristics, like molecular adsor…
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…