data-driven prediction of transesterification reactions: analyzing zirconium-based metal-organic framework catalysts with machine learning models

This research study focuses on the utilization of machine learning models to predict transesterification reactions using zirconium-based metal-organic framework (mof) as a catalyst. various machine learning algorithms, including multiple linear regression (mlr), polynomial regression (plr), decision tree (dt), random forest (rf), adaboost (ab), xgboost (xgb), k-nearest neighbors (knn), support vector machines (svm), and artificial neural networks (ann), were employed to analyze the collected data. the performance of each model was evaluated using mean accuracy and median accuracy metrics. among the tested models, xgboost exhibited the highest predictive accuracy, with a mean accuracy of 91.07% and a median accuracy of 96.72%. these results demonstrate that xgboost effectively captures the intricate relationships between the input features and the outcomes of transesterification reactions. furthermore, a feature importance analysis conducted using xgboost revealed the relative significance of various factors in the reaction process. the analysis revealed that the preparation method held the highest importance. the factors ranked by importance were: preparation method, catalyst loading, alcohol type, reaction temperature, mof, reaction time, acid functionalized, base functionalized, metal precursor, and linker. these findings enhance our understanding of transesterification reactions catalyzed by zirconium-based mofs and highlight the effectiveness of machine learning models in predicting their outcomes.