Aug 2026· Journal of Physical Chemistry Letters· Vol 17 36, pp.
10546-10554
· 0 citations· 47 references
Medicine
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.
Manganese-based layered oxides have emerged as promising cathode materials for potassium-ion batteries owing to their low cost, environmental benignity, structural diversity, and high theoretical capacity, yet suffer from poor rate performance. Compositional regulation offers an effective strategy to address this limit...
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Hard carbons are established anodes for lithium-ion batteries and leading candidates for sodium-ion batteries, yet their electrochemical performance is governed by a heterogeneous network of graphitic domains, defects, and nanopores that conventional atomistic methods cannot model at the required length scales. We comb...
Harry Mclean, Aiden Daniel Emery, T. T. Walton et al.· 0 citations
The vast, unexplored synthesis space of LNMCO cathode materials contains potential solutions to the long-standing trade-off between energy density and stability. To navigate this high-dimensional space, a predictive tool capable of accurately mapping the complex relationships between synthesis parameters and electroche...
Chenfeng Wang, Quan-Jiang Li, Lihong Zhang et al.· Advances in Materials· 0 citations
ABSTRACT One of the most formidable challenges in materials chemistry is the rational design of functionalities capable of dramatically enhancing performance. However, it is well-known that the discovery of promising materials often requires several decades of continuous trial-and-error. Herein, we show an interpretabl...
Wenqin Peng, S. Hayashi, Abraham Castro Garcia et al.· Science and Technology of Ad...· 0 citations
Metal–nitrogen–carbon dual-atom catalysts (M1/M2–N–C DACs) have emerged as promising alternatives to Pt-based catalysts for the oxygen reduction reaction (ORR), yet their rational discovery is hindered by an enormous chemical and structural design space. Here, we develop a physics-informed machine learning (ML) frame...
Prajeet Oza, Victor Fung, Guo-Xiang Hu· ACS Catalysis· 0 citations
Ni-rich layered oxide cathode materials have emerged as promising candidates for next-generation mainstream high-energy nonaqueous lithium-based batteries because of their inherent advantages in terms of specific capacity. However, the delicate layered structure is more susceptible to both crystal and morphological str...
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