3 citations · 3 across the 9 of their papers we have counts for
11 papers
All-electron machine-learned orbital-free density functional for periodic CHNO systems
Marc K. Ickler, Andreas Hermann, Fred A. Hamprecht
We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems. Electrostatics are evaluated analytically in an atom-centered G…
Orbital-Free Surrogate Functionals Yield Transferable Interatomic Potentials and Electron Densities
Simon Wagner, Marc K. Ickler, Manuel V. Klockow +2
Orbital-free density functional theory seeks to compute the energy of an electronic system directly from its electron density, avoiding one-electron wave functions and thereby offe…
Rotational Equivariance in Machine Learning: A Comprehensive Tutorial
Peter Lippmann, Fred A. Hamprecht
Rotational symmetry is one of the most important structural principles in machine learning on 3D data. In applications ranging from physics and materials science to 3D computer vis…
Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
Roman Remme, Fred A. Hamprecht
We introduce surrogate functionals: machine-learned energy functionals for orbital-free density functional theory (OF-DFT) which are defined not by universal fidelity to a physical…
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.…
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher +18
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working…
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…
Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing
Peter Lippmann, Gerrit Gerhartz, Roman Remme +1
In numerous applications of geometric deep learning, the studied systems exhibit spatial symmetries and it is desirable to enforce these. For the symmetry of global rotations and r…
SynCellFactory: Generative Data Augmentation for Cell Tracking
Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht
Cell tracking remains a pivotal yet challenging task in biomedical research. The full potential of deep learning for this purpose is often untapped due to the limited availability…