fig1

Figure 1. (A) The calculation process of ML. (B) Correlation matrix of Pearson coefficients for the ten selected feature descriptors, where positive and negative values indicate positive and negative correlations, and numeric values represent the degree of correlation. (C) Proportional classification of the ΔPCE data. (D) Accuracy calculated by a single ML model. (E) Ranking of the importance of different eigenvectors in type II. The SHAP value represents a positive or negative contribution to the efficiency of the device. Red corresponds to high-value features, while blue corresponds to low-value features.