MEMFNet: Toward a knowledge-guided paradigm for interpretable electrochemical performance prediction

Nano Energy 2026

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Authors: Kun Han, J.-L. Yang, Chenyu Wang, Junfeng Li, Junfeng Li, Zhijing Zhu, Wenjie Mai, Jinliang Li, Jinliang Li, Guang Yang, Likun Pan

DOI: 10.1016/j.nanoen.2026.111735

Cited by: 3

References: 38

Fields of Study & Subjects

Machine Learning in Materials Science CO2 Reduction Techniques and Catalysts Electrocatalysts for Energy Conversion

References 38