This approach contains information about the bond connectivity (polymerization point "*") along the polymer chain, potentially enhancing the accuracy of ML models according to our previous studies. The SMILES notation of polymer structures is transformed into a detailed vectorized format using MFF to enhance the input representation for ML models. Initially, the SMILES strings are converted into molecular structures using RDKit. Morgan fingerprints are then computed for each polymer with a radius of 3, capturing information about atom types and substructures. Unlike prominence as a robust approach for quantifying prediction uncertainty in ML. This technique entails the creation of multiple distinct neural networks, each sharing the same architecture but trained independently using the same dataset. The ensemble yields a collection of predictions, which, under the assumption of normality, can be represented by a Gaussian distribution characterized by its mean and standard deviation. The inherent stochastic nature of neural network training, influenced by random initialization of parameters and convergence to diverse local optima, results in variations in the weights and biases across the ensemble models. Consequently, this diversity allows the ensemble method to effectively capture and quantify epistemic uncertainty, addressing the intrinsic limitations of single-model predictions by providing a more comprehensive uncertainty estimate. Ensemble was first introduced and discussed to improve the accuracy and diversity of a prediction. And later ensemble is found to be well-suited for UQ of neural networks. But it comes along with significantly increased computational effort and memory. 59