all AI news
GemNet: Universal Directional Graph Neural Networks for Molecules. (arXiv:2106.08903v6 [physics.comp-ph] UPDATED)
Jan. 12, 2022, 2:11 a.m. | Johannes Klicpera, Florian Becker, Stephan Günnemann
cs.LG updates on arXiv.org arxiv.org
Effectively predicting molecular interactions has the potential to accelerate
molecular dynamics by multiple orders of magnitude and thus revolutionize
chemical simulations. Graph neural networks (GNNs) have recently shown great
successes for this task, overtaking classical methods based on fixed molecular
kernels. However, they still appear very limited from a theoretical
perspective, since regular GNNs cannot distinguish certain types of graphs. In
this work we close this gap between theory and practice. We show that GNNs with
directed edge embeddings and …
arxiv graph graph neural networks networks neural networks physics
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Engineer
@ Parker | New York City
Sr. Data Analyst | Home Solutions
@ Three Ships | Raleigh or Charlotte, NC