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

doi:10.1016/j.nanoen.2026.111735

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

2026-01-16

0

PRE

AI

DOI：10.1016/j.nanoen.2026.111735

Original

Original request for help

Save

Abstract

En 中文

• Dual-pathway architecture separating static material properties from dynamic electrochemical evolution reduces prediction error by 48.64% compared to state-of-the-art methods • ·Physics-constrained graph connectivity restricting message passing to metal-oxygen bonds aligns learned representations with established electrochemical principles • Hierarchical attention mechanisms spontaneously segregate atomic bonding, redox activity, and ion transport across network layers without explicit programming • Knowledge-guided architecture transforms machine learning from black-box predictor to interpretable system revealing mechanistic insights into battery electrochemistry

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

Abstract

Journal

Organization