3 papers
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…
q-bio.QM2024
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…
cs.LG2023
Almost Equivariance via Lie Algebra Convolutions
Daniel McNeela
Recently, the equivariance of models with respect to a group action has become an important topic of research in machine learning. Analysis of the built-in equivariance of existing…