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

The Journal of Physical Chemistry Letters. 2026.

Meng K., Vasenko A., Chulkov E., Long R.

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.

Keywords: cathodes, sodium-ion batteries, First-principles calculations, Натрий-ионные аккумуляторы, первопринципные расчёты, катоды, Data-driven discovery, Поиск на основе данных

Publication based on the results of: Quantum devices and technologies of the new generation (2025)