arXiv subjects

Eike Eberhard

Publications and source records attributed to Eike Eberhard.

3 recordsLinked to original sources

arXiv2026

3D Molecule Generation from Rigid Motifs via SE(3) Flows

Three-dimensional molecular structure generation is typically performed at the level of individual atoms, yet molecular graph generation techniques often consider fragments as their structural units. Building on the advances in frame-based protein structure generation, we extend these fragmentation ideas to 3D, treating general molecules as sets of rigid-body motifs. Utilising this representation, we employ SE(3)-equivariant generative modelling for de novo 3D molecule generation from rigid motifs. In our evaluations, we observe comparable or superior results to state-of-the-art across benchmarks, surpassing it in atom stability on GEOM-Drugs, while yielding a 2x to 10x reduction in generation steps and offering 3.5x compression in molecular representations compared to the standard atom-based methods.

cs.LG↗

arXiv2025

Learning Equivariant Non-Local Electron Density Functionals

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-designed approximations suffer from insufficient accuracy, limited scalability, or dependence on costly reference data. To address these issues, we introduce Equivariant Graph Exchange Correlation (EG-XC), a novel non-local XC functional based on equivariant graph neural networks (GNNs). Where previous works relied on semi-local functionals or fixed-size descriptors of the density, we compress the electron density into an SO(3)-equivariant nuclei-centered point cloud for efficient non-local atomic-range interactions. By applying an equivariant GNN on this point cloud, we capture molecular-range interactions in a scalable and accurate manner. To train EG-XC, we differentiate through a self-consistent field solver requiring only energy targets. In our empirical evaluation, we find EG-XC to accurately reconstruct `gold-standard' CCSD(T) energies on MD17. On out-of-distribution conformations of 3BPA, EG-XC reduces the relative MAE by 35% to 50%. Remarkably, EG-XC excels in data efficiency and molecular size extrapolation on QM9, matching force fields trained on 5 times more and larger molecules. On identical training sets, EG-XC yields on average 51% lower MAEs.

cs.LG↗

arXiv2021

EOS -- A Software for Flavor Physics Phenomenology

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 physics, Bayesian inference of theory parameters from experimental and theoretical likelihoods, and simulation of pseudo events for a number of signal processes. EOS ensures high-performance computations through a C++ back-end and ease of usability through a Python front-end. To achieve this flexibility, EOS enables the user to select from a variety of implementations of the relevant decay processes and hadronic matrix elements at run time. In this article, we describe the general structure of the software framework and provide basic examples. Further details and in-depth interactive examples are provided as part of the EOS online documentation.

hep-ph↗