We use the above-mentioned properties in feature space to train a random forest regression model. In general, sample properties (features) can nonlinearly influence activation energy and shift model prediction up or down. Our feature space has N samples × N f eatures = 3354 variables that all contribute to the predictability. We used the SHapley Additive exPlanations (SHAP) analysis to decode feature importance and interpret the ML model. The basic principle behind this analysis is to break down a model's prediction by estimating each feature's contribution to the prediction based on its missingness. This breakdown indicates how important each feature is in determining the model's final activation energy forecast. By quantifying the impact of each feature, SHAP analysis offers a comprehensive and intuitive understanding of the model's behavior.