Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification.

To foster greater trust and adoption of machine learning models, particularly neural networks, it is essential to develop approaches that quantify and report epistemic uncertainties alongside random uncertainties, which often affect the accuracy of Recurrent Neural Networks (RNNs). Addressing these challenges, this study proposes a hybrid approach integrating Bayesian techniques and deep learning to improve the classification of nanocomposites with a focus on evaluating their conductivity properties. The proposed framework begins with a Bayesian Network (BN) model, which provides probabilistic insights into the conductive behavior of nanocomposites by analyzing the distribution and interaction of their constituent nanoparticles. This probabilistic foundation is complemented by a Recurrent Neural Network (RNN) based on the Transformer architecture, which enhances classification accuracy by capturing sequential dependencies and complex data patterns. The hybrid model combines the probabilistic reasoning capabilities of BNs with the deep learning strengths of RNNs, yielding a more robust and adaptable classification methodology. While this study primarily focuses on methodological advancements, experimental results demonstrate that the hybrid model significantly outperforms individual approaches in terms of key evaluation metrics. This integrated framework thus represents a promising step toward improving the predictive classification of nanocomposite conductivity, offering a balance between probabilistic interpretability and data-driven accuracy.