62 citations · 62 across the 5 of their papers we have counts for
4 papers · 1 filter
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…