5 papers
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…
Anharmonic infrared spectra of cationic pyrene and superhydrogenated derivatives
Zeyuan Tang, Frederik G. Doktor, Rijutha Jaganathan +4
Studying the anharmonicity in the infrared (IR) spectra of polycyclic aromatic hydrocarbons (PAHs) at elevated temperatures is important to understand vibrational features and chem…
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…