From Small Data to High Capacity: Symbolic Learning of Sodium-Ion Cathode Materials.

Authors

Kong Meng, Andrey S Vasenko, Evgueni V Chulkov, Run Long

Published in

The journal of physical chemistry letters. Volume 17. Issue 36. Pages 10546-10554. Sep 10, 2026.

Abstract

Data-driven discovery of sodium-ion cathodes is often limited by small data sets and the poor interpretability of graph neural networks (GNNs). Here, we developed Symbol_ETR, an interpretable framework combining symbolic regression with ensemble learning. By constructing explicit nonlinear descriptors, symbolic regression extra trees regression (Symbol_ETR) achieves a test R2 of 0.90 for average voltage prediction and outperforms representative GNN models in the small-data regime. High-throughput screening of the Materials Project database identified Na5Co2S5 as a promising cathode candidate with a theoretical capacity of 340.86 mAh/g. First-principles calculations indicate a metallic electronic character and reveal weakly hybridized S 3p states near the Fermi level at Na-rich sulfur sites. Their evolution during desodiation supports sulfur-dominated charge compensation and anion redox activity. This work establishes an accurate and interpretable strategy for small-data material screening and the discovery of high-capacity sodium-ion cathodes.

PMID:
42720337
Bibliographic data and abstract were imported from PubMed on 10 Sep 2026.

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