62 citations · 62 across the 5 of their papers we have counts for
6 papers
Physics-Aligned Electronic Ground-State Learning Improves Generalization
Eike S. Eberhard, Xaver Kainz, Viktor Kotsev +2
Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generalize out-of-distribution. We p…
Derivative Informed Learning of Exchange-Correlation Functionals
Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary +5
Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still…
Transferable SCF-Acceleration through Solver-Aligned Initialization Learning
Eike S. Eberhard, Viktor Kotsev, Timm Güthle +1
The cost of Kohn-Sham density functional theory (KS-DFT) calculations scales with the number of solver iterations, which depends on the quality of the initial guess. Machine learni…
3D Molecule Generation from Rigid Motifs via SE(3) Flows
Roman Poletukhin, Marcel Kollovieh, Eike Eberhard +1
Three-dimensional molecular structure generation is typically performed at the level of individual atoms, yet molecular graph generation techniques often consider fragments as thei…
Learning Equivariant Non-Local Electron Density Functionals
Nicholas Gao, Eike Eberhard, Stephan Günnemann
The accuracy of density functional theory hinges on the approximation of non-local contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-des…
EOS -- A Software for Flavor Physics Phenomenology
Danny van Dyk, Frederik Beaujean, Thomas Blake +17
EOS is an open-source software for a variety of computational tasks in flavor physics. Its use cases include theory predictions within and beyond the Standard Model of particle phy…