26 citations · 74 across the 8 of their papers we have counts for
8 papers
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…
HIP: Hessian Interatomic Potentials without derivatives
Andreas Burger, Luca Thiede, Nikolaj Rønne +5
Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally 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…
Augmentation of Universal Potentials for Broad Applications
Joe Pitfield, Florian Brix, Zeyuan Tang +4
Universal potentials open the door for DFT level calculations at a fraction of their cost. We find that for application to systems outside the scope of its training data, CHGNet\ci…
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…
Atomistic structure search using local surrogate mode
Nikolaj Rønne, Mads-Peter V. Christiansen, Andreas Møller Slavensky +5
We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based…
Atomistic Global Optimization X: A Python package for optimization of atomistic structures
Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer
Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and…