Zobrazeno 1 - 10
of 7 796
pro vyhledávání: '"Bjørk, A."'
Autor:
Pomar, Thierry Désiré, Steegemans, Tristan, Kumar, Sreejith, Bjørk, Rasmus, Lei, Zijin, Cheah, Erik, Schott, Rüdiger, Bøggild, Peter, Pryds, Nini, Wegscheider, Werner, Christensen, Dennis Valbjørn
Magnetometers based on the extraordinary magnetoresistance (EMR) effect are promising for applications which demand high sensitivity combined with room temperature operation but their application for magnetic field sensing requires further optimizati
Externí odkaz:
http://arxiv.org/abs/2410.17713
Autor:
Pitfield, Joe, Brix, Florian, Tang, Zeyuan, Slavensky, Andreas Møller, Rønne, Nikolaj, Christiansen, Mads-Peter Verner, Hammer, Bjørk
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\cite{deng2023chgnet} has the potential to succeed out of the box, but ca
Externí odkaz:
http://arxiv.org/abs/2407.14288
Publikováno v:
J. Chem. Phys. 161, 014713 (2024)
We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a
Externí odkaz:
http://arxiv.org/abs/2407.13471
Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian Process Regression type MLIP a stochastic uncertainty measure aki
Externí odkaz:
http://arxiv.org/abs/2407.12525
We present a generative model that amortises computation for the field around e.g. gravitational or magnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources and field evaluat
Externí odkaz:
http://arxiv.org/abs/2405.05981
Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning
Externí odkaz:
http://arxiv.org/abs/2404.12856
Autor:
Kantas, Christos, Antoniussen, Bjørk, Andersen, Mathias V., Munksø, Rasmus, Kotnala, Shobhit, Jensen, Simon B., Møgelmose, Andreas, Nørgaard, Lau, Moeslund, Thomas B.
Publikováno v:
2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)
Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper, we show that a sufficiently advanced classifier can yield equivalent
Externí odkaz:
http://arxiv.org/abs/2403.14439
Publikováno v:
J. Chem. Phys. 159, 024123 (2023)
Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for the global minimum energy (GM) structure. In this work, we discuss a t
Externí odkaz:
http://arxiv.org/abs/2402.18338
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 including masked atoms and $z$-directional confinement. Using a rotati
Externí odkaz:
http://arxiv.org/abs/2402.17404
Autor:
Ghita, Ahmed, Antoniussen, Bjørk, Zimmer, Walter, Greer, Ross, Creß, Christian, Møgelmose, Andreas, Trivedi, Mohan M., Knoll, Alois C.
The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remains. In this work, we fill the research gap using active learning for m
Externí odkaz:
http://arxiv.org/abs/2402.03235