MATH · IN · MODELS

SchNet (continuous-filter convolutional GNN for molecules)

research/foundational-architecture

Structures found in this family (2)

Intrinsic-dimension profile across depth, Linear Separability

By model (1)

SchNet-family GNN, trained on QM9 (El-Samman, Husain, Huynh, De Castro, Morton & De Baerdemacker)
Intrinsic-dimension profile across depth, Linear Separability

Observations (1)

A real SchNet-family GNN's 128-parameter QM9 molecular embedding space reduces to around 5 effective parameters, with sharp linear boundaries separating chemical moieties

Papers

Global geometry of chemical graph neural network representations in terms of chemical moieties (2024)