2 papers
q-bio.QM2025
Product Manifold Representations for Learning on Biological Pathways
Daniel McNeela, Frederic Sala, Anthony Gitter
Machine learning models that embed graphs in non-Euclidean spaces have shown substantial benefits in a variety of contexts, but their application has not been studied extensively i…
physics.chem-ph2024
OpenQDC: Open Quantum Data Commons
Cristian Gabellini, Nikhil Shenoy, Stephan Thaler +5
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and fo…