4 papers
Equivariance by Local Canonicalization: A Matter of Representation
Gerrit Gerhartz, Peter Lippmann, Fred A. Hamprecht
Equivariant neural networks offer strong inductive biases for learning from molecular and geometric data but often rely on specialized, computationally expensive tensor operations.…
Lorentz-Equivariance without Limitations
Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht +5
Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts…
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant
Jonas Spinner, Luigi Favaro, Peter Lippmann +4
Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choic…
Stable and Accurate Orbital-Free DFT Powered by Machine Learning
Roman Remme, Tobias Kaczun, Tim Ebert +10
Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to th…