1 citations · 1 across the 6 of their papers we have counts for
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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…
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,…
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…