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
Efficient exploration of chemical space with machine-learning interatomic potentials (MLIPs) requires a comprehensive, application-focused training database. Solid-state electrolytes for Li-ion batteries have attracted growing attention owing to their ability to improve safety and energy density. Among these, halide-ba...
Chiku Parida, Arghya Bhowmik, Juan Maria Garcia Lastra· Machine Learning: Science an...· 0 citations
This work introduces ANCHOR, a GRPO composition policy trained with multi-objective rewards around a frozen CSP model, and continuous adaptive novelty (CAN), a graded novelty score against known structures and a growing discovery history.
Emma Lei Hovmand, J. Elsborg, Melih Kandemir et al.· 0 citations
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both liquid environments, the solid and the interface itself. We present SoLiD26, a curated solid-liquid interface dataset, c...
Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan et al.· 0 citations
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
This work develops and operationalizes uncertainty-aware machine-learned interatomic potentials (MLIPs), including general-purpose foundation models for stable molecular dynamics across diverse chemistries, alongside evidence-based limits of universality in energy materials.
