The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has ...

Graph neural networks (GNNs) are specialised deep learning architectures designed to operate on data represented as graphs, where entities are modelled as nodes and relationships as edges. In ...

In a major step toward more reliable AI-assisted molecular design, researchers from National Taiwan University have demonstrated that incorporating uncertainty quantification (UQ) into graph neural ...