High‐Throughput Design of Active MXene Catalysts for Li─O2 Battery Using Machine Learning

High‐Throughput Design of Active MXene Catalysts for Li─O2 Battery Using Machine Learning

Advanced Functional Materials

10.1002/adfm.202532003

https://doi.org/10.1002/adfm.202532003

Peng Zhang, Yixin Yan, Dominik Legut, Yuanjian Li, Zhihao Li, Chao Lin, Xiang Feng, Shucheng Wang, Yifan Wu, Shenqi Wang, Ruifeng Zhang, Zhi Wei Seh, Qianfan Zhang

20 February 2026

Zhang, P., Yan, Y., Legut, D., Li, Y., Li, Z., Lin, C., Feng, X., Wang, S., Wu, Y., Wang, S., Zhang, R., Seh, Z. W., & Zhang, Q. (2026). High-Throughput Design of Active MXene Catalysts for Li─O2 Battery Using Machine Learning. Advanced Functional Materials, 36(39). Portico. https://doi.org/10.1002/adfm.202532003

The performance of lithium–oxygen (Li─O2) batteries is limited by sluggish reaction kinetics, leading to issues such as poor reversibility and severe parasitic reactions. This necessitates advanced catalysts like MXenes, but their vast compositional diversity and complex structure‐activity relationships hinder traditional discovery approaches. Herein, we employ an integrated high‐throughput workflow (HTW) and machine learning (ML) framework for Li batteries for the first time to systematically investigate 2D transition metal carbides/nitrides MXenes‐based catalysts. We defined a virtual compositional space of ∼2 million MXene candidates. Guided by a combinatorial enumeration and subsequent rule‐based screening, we down‐selected this space to an HTW design set of 4896 unique MXene configurations for computation. Our developed Light Gradient Boosting Machine model achieved superior accuracy (MAE = 0.32 eV) in predicting reaction free energy change across four key steps, enabling the identification of exceptional catalysts including Mo3C2Cl2 which exhibits an ultra‐low overpotential of 0.01 V. Our interpretability analysis reveals the intricate mechanisms by which different electronegativity terminals modulate the electronic structure and reaction mechanisms of MXenes. This work establishes an efficient computational reference for accelerating the discovery of advanced energy materials and provides fundamental insights into structure‐activity relationships in electrocatalysis.

Publisher Copyright

This research / project is supported by the Singapore National Research Foundation - NRF Investigatorship NRF-NRFI09-0002
Grant Reference no. : NRF-NRFI09-0002

This is the peer reviewed version of the following article: Zhang, P., Yan, Y., Legut, D., Li, Y., Li, Z., Lin, C., Feng, X., Wang, S., Wu, Y., Wang, S., Zhang, R., Seh, Z. W., & Zhang, Q. (2026). High‐Throughput Design of Active MXene Catalysts for Li─O2 Battery Using Machine Learning. Advanced Functional Materials, 36(39). Portico. https://doi.org/10.1002/adfm.202532003, which has been published in final form at https://doi.org/10.1002/adfm.202532003. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

https://oar.a-star.edu.sg/communities-collections/articles/23493

1616-301X
1616-3028

Institute of Materials Research and Engineering