Predicting and Understanding Work Functions of Double Transition Metal MXenes via Interpretable Machine Learning...

In this study, we employed interpretable machine learning models to predict and understand the work functions of double transition metal MXenes with the formula (M1)₂(M2)X₂O₂. We created a comprehensive data set of 242 structures covering 11 transition metals and two X elements (C, N) using first-principles calculations. Various machine learning methods, including linear regression, support vector regression, random forest regression, and artificial neural networks, were thoroughly evaluated. The random forest model achieved the best performance on the test set, with a mean absolute error of 0.17 ± 0.02 eV, root-mean-square error of 0.24 ± 0.03 eV, and R² of 0.86 ± 0.03. The feature importance analysis revealed a hierarchical influence mechanism: the properties of the outer transition metal (M1) dominate the work function, followed by the X element, while the inner transition metal (M2) has minimal impact. Furthermore, we employed the Sure Independence Screening and Sparsifying Operator method to derive analytical expressions that relate element features to work functions, achieving comparable accuracy (R² = 0.82 ± 0.04) while providing physical insights. Our findings not only enable rapid prediction of MXene work functions but also provide valuable guidance for the rational design of MXene-based materials.