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Shang et al. J. Mater. Inf. 2025, 5, 52 Journal of
DOI: 10.20517/jmi.2025.36
Materials Informatics

Research Article Open Access

Stacked machine learning for accurate and
interpretable prediction of MXenes’ work function

1,2
2,*
1
1,*
2
1,2
Lijun Shang , Yongli Yang , Yadong Yu , Pan Xiang , Li Ma , Zhonglu Guo , Mengyan Dai 2,*
1
Hebei Key Laboratory of Boron Nitride Micro and Nano Materials, School of Materials Science and Engineering, Hebei University
of Technology, Tianjin 300130, China.
2
Chemical Defense Institute, Academy of Military Sciences, Beijing 102205, China.
* Correspondence to: Prof. Yadong Yu, Prof. Mengyan Dai, Chemical Defense Institute, Academy of Military Sciences, Beijing
102205, China. E-mail: yuyadong36@163.com; daidecai0558@163.com; Prof. Zhonglu Guo, Hebei Key Laboratory of Boron
Nitride Micro and Nano Materials, School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300130,
China. E-mail: zlguo@hebut.edu.cn
How to cite this article: Shang, L.; Yang, Y.; Yu, Y.; Xiang, P.; Ma, L.; Guo, Z.; Dai, M. Stacked machine learning for accurate and
interpretable prediction of MXenes’ work function. J. Mater. Inf. 2025, 5, 52. https://dx.doi.org/10.20517/jmi.2025.36
Received: 11 May 2025 First Decision: 2 Jul 2025 Revised: 16 Jul 2025 Accepted: 28 Jul 2025 Published: 10 Nov 2025
Academic Editor: Sergei Manzhos Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
MXenes, with tunable compositions and rich surface chemistry, enable precise control of electronic, optical, and
mechanical properties, making them promising materials in electronics and energy-related applications. In
particular, the work function plays a critical role in determining their physicochemical properties. However, the
accurate prediction of the work function of MXenes with machine learning (ML) remains challenging due to the
lack of robust models with high accuracy and interpretability. To this end, we propose a stacked model and
introduce high-quality descriptors constructed via Sure Independence Screening and Sparsifying Operator method
to improve the prediction accuracy of the work function of MXenes in this work. The stacked model initially
generates predictions from multiple base models, and then employs these predictions as inputs to a meta-model
for secondary learning, thereby enhancing both predictive performance and generalization capability. The results
show that by integrating the high-quality descriptors, the model’s performance improves significantly, yielding a
coefficient of determination of 0.95 and mean absolute error of 0.2, respectively. Last but not least, we
demonstrate that MXenes’ work functions are predominantly governed by their surface functional groups, where
SHapley Additive exPlanations value analysis quantitatively resolves the structure–property relationship between
surface functional groups and the work function of MXenes. Specifically, O terminations can lead to the highest
work functions, while OH terminations result in the lowest value (over 50% reduction), and transition metals or
C/N elements have a relatively smaller effect. This work achieves an optimal balance between accuracy and

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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