26 citations · 74 across the 8 of their papers we have counts for
4 papers · 1 filter
GO-Diff: Data-free and amortized global structure optimization
Nikolaj Rønne, Tejs Vegge, Arghya Bhowmik
We introduce GO-Diff, a diffusion-based method for global structure optimization that learns to directly sample low-energy atomic configurations without requiring prior data or exp…
Atomistic Generative Diffusion for Materials Modeling
Nikolaj Rønne, Bjørk Hammer
We present a generative modeling framework for atomistic systems that combines score-based diffusion for atomic positions with a novel continuous-time discrete diffusion process fo…
Efficient ensemble uncertainty estimation in Gaussian Processes Regression
Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer
Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaus…
Generative diffusion model for surface structure discovery
Nikolaj Rønne, Alán Aspuru-Guzik, Bjørk Hammer
We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by…