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Page 6 of 15 Shang et al. J. Mater. Inf. 2025, 5, 52 https://dx.doi.org/10.20517/jmi.2025.36

Figure 2. Scatter plots comparing predicted and actual work function values. (A) General RF model; (B) Stacked model with RF as the
meta-model; (C) Stacked model with RF as the meta-model and General RF model; and (D) Stacked model with RF as the meta-model
and incorporating SISSO descriptors. The color bar represents the deviation z between calculated and predicted values. RF: Random
forest; SISSO: Sure Independence Screening and Sparsifying Operator.

2C (fold 3), a modest decrease in error dispersion is also observed. This clearly illustrates the stacked
model’s capacity to enhance prediction accuracy for the work function of MXenes by leveraging the
advantages of multiple base models.

To establish a crucial foundation for subsequent interpretable ML analysis, we integrated the
aforementioned SISSO descriptors into the dataset. Considering the model complexity arising from an
excessive number of features and the difficulty of improving model accuracy and interpretability without
significantly increasing complexity, we moderately selected three SISSO descriptors that exhibit optimal
correlation with the work function, as detailed in Table 1.

After incorporating key effective descriptors, these SISSO descriptors significantly improved the model’s
interpretability, enabling it to capture subtle yet influential data patterns. Consequently, the MAE of the
2
improved stacked model decreases from 0.22 to 0.20, and the R increases from 0.91 to 0.95 as shown in
Figure 2D. Moreover, z is used as a variable and color mapping to deviation, with lighter data points
indicating greater bias, which is expressed as follows: