e-3-equivariant-graph-neural-network-ed897a41·2 events·first seen Aliases: E(3) equivariant graph neural network, Equivariant Graph Neural Network
Researchers introduce EnsembleEGNN, a molecular ensemble foundation model that encodes conformational ensembles of cyclic peptides using Equivariant Graph Neural Networks with Set Attention Block pooling. The model is pretrained on the CREMP cyclic peptide ensemble dataset via multi-task self-supervised objectives including masked token recovery and coordinate reconstruction. On the CREMP-CycPeptMPDB benchmark, the pretrained model achieves R²=0.477 versus complete failure (R²=0.005) when trained from scratch, and a hybrid model combining EnsembleEGNN with a BERT sequence encoder reaches R²=0.538, demonstrating that thermodynamically-informed conformational ensemble embeddings meaningfully improve property prediction.
The paper introduces MSN (Magnetic Structure Network), an E(3) equivariant graph neural network that predicts collinear and non-collinear magnetic structures directly from atomic crystal coordinates. Trained on experimentally determined structures from the MAGNDATA database, it uses a novel Primitive Modulated Structure Representation (PMSR) to handle both commensurate and incommensurate magnetic orders in a unified framework without symmetry assumptions. The model achieves near-experimental accuracy across diverse magnetic structure types, offering a scalable alternative to costly experiments and computationally demanding first-principles methods for magnetic materials discovery.