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physics.comp-ph2025
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…
physics.comp-ph2024
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…
physics.comp-ph2024
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…