Author

J. Elsborg

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Preprint Sep 2026

Improved Learning of Molecular Energetics Through an Electron-Wise Joint Charge Density and Energy Objective

We present a graph neural network-based learning framework trained to jointly predict electron densities and molecular energies. The model is trained to predict electron densities calculated with a Generalized Gradient Approximation (GGA) functional while simultaneously learning to predict molecular energies obtained w...

V. Ionas, J. Elsborg, Felix Ærtebjerg et al. · 0 citations

#machine learning Preprint Sep 2026

Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors

De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue that discovery is better served by separating the two. A crystal structure prediction (CSP) model is a physical prior that should be improved by likelihood training, while...

Emma Lei Hovmand, J. Elsborg, Melih Kandemir et al. · 0 citations

#machine learning Preprint Sep 2026

Complete Neural Electronic Initialization Accelerates Materials DFT

This work introduces AugNet, a general equivariant model for PAW augmentation occupancies, and the first general spin density model for materials, which predicts the smooth spin-difference density and spin-difference PAW augmentation occupancies using predicted magnetic moments to constrain the global magnetic state.

Felix Ærtebjerg, J. Elsborg, Arghya Bhowmik · 0 citations