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

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 accurately predicting 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 address this, we propose a stacked model and introduce high-quality descriptors constructed via Sure Independence Screening and Sparsifying Operator (SISSO) 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, 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 an R² of 0.95 and mean absolute error (MAE) of 0.2, respectively. Last but not least, we demonstrate that MXenes' work functions are predominantly governed by their surface functional groups, where SHAP 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 (TM) or C/N elements have a relatively smaller effect. This work achieves an optimal balance between accuracy and interpretability in machine learning predictions of MXenes' work functions, providing both fundamental insights and practical tools for materials discovery.