Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations

arXiv:2211.03226v3 Announce Type: replace-cross
Abstract: The difficult problem of relating the static structure of glassy liquids and their dynamics is a good target for Machine Learning, an approach which excels at finding complex patterns hidden in data. Indeed, this approach is currently a hot topic in the glassy liquids community, where the state of the art consists in Graph Neural Networks (GNNs), which have great expressive power but are heavy models and lack interpretability. Inspired by recent advances in the …

abstract arxiv community cond-mat.dis-nn cond-mat.soft cs.lg data dynamics good graph graph neural networks hidden hot indeed machine machine learning networks neural networks patterns rotation type