Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.
Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs
Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have used ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results