From the 1 of 15 linked papers with an AI index.
1 citations · 1 across the 3 of their papers we have counts for
15 papers
How Molecular Generative Models Organize Molecular Identity
Raul Ortega-Ochoa, Tejs Vegge, Jens S. Bakander +3
Generative models for matter are often evaluated as samplers over output representations, and their latent spaces are commonly used as proxies for navigating chemical space. Much l…
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen
MolMiner is an autoregressive model that builds molecules by attaching fragments in a geometry‑aware, symmetry‑respecting way, while allowing users to control multiple physicochemi…
HIP: Hessian Interatomic Potentials without derivatives
Andreas Burger, Luca Thiede, Nikolaj Rønne +6
Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally exp…
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
Jonas Elsborg, Felix Ãrtebjerg, Luca Thiede +3
We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in rea…
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning
Bjarke Hastrup, Francois Cornet, Tejs Vegge +1
Discovering novel stable molecules without training data remains a grand scientific challenge. Current molecular generative models are trained on large, pre-curated datasets, which…
A large scale multi-modal workflow for battery characterization: from concept to implementation
François Cadiou, Cinthya Herrera, Duncan Atkins +32
The development of material acceleration platforms in battery research requires integrating complementary techniques and correlating heterogeneous experimental datasets. Here, this…