Traditionally, CNNs perform a classification task by outputting probabilities of particular crystal systems (i.e., opinions), and a classification is usually achieved by applying a SoftMax activation function at the output layer and minimizing the cross-entropy loss function. Conventionally, it is equivalent to minimizing the cross-entropy loss function and maximizing the likelihood, assuming a multinomial distribution with the parameters as class probabilities. However, such a setup does not account for the confidence of forming an opinion (i.e., the uncertainty). Uncertainty quantification is desired in our MVOF-ML framework since it is used to guide the fusion of the opinions. Therefore, we trained our CNN using the principle of evidential deep learning . The uncertainty is explicitly