Integrating uncertainty quantification into deep learning

In this project, we are working on developing a novel deep-learning framework that incorporates quantified uncertainty for predicting the mechanical properties of nanocomposite materials, specifically taking into account their morphology and composition. Due to the intricate microstructures of nanocomposites and their dynamic changes under diverse conditions, traditional methods, such as molecular dynamics simulations, often impose significant computational burdens. Our machine learning models, trained on comprehensive material datasets, provide a lower computational cost alternative, facilitating rapid exploration of design spaces and more reliable predictions. Furthermore, we integrate uncertainty quantification into our models, thereby providing confidence intervals for our predictions and making them more reliable. This project aims to pave the way for advancements in predicting the properties of advanced materials and could potentially be expanded to cover a broad spectrum of materials in the future.