Bjørk Hammer

Aarhus University — Denmark

Physics and Astronomy · Physical Sciences

86h-index56.4kcitations296works3.72yr avg
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Research Topics

Advanced Chemical Physics Studies(121), Catalytic Processes in Materials Science(96), Surface Chemistry and Catalysis(46), Machine Learning in Materials Science(42), Molecular Junctions and Nanostructures(38)

Publications296 total

Active Δ-learning with universal potentials for global structure optimization
Physical Chemistry Chemical Physics·2025
Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
Machine Learning Science and Technology·2025Open Access
Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
arXiv (Cornell University)·2025Open Access
Atomistic Generative Diffusion for Materials Modeling
arXiv (Cornell University)·2025Open Access
Active Δ-learning with universal potentials for global structure optimization
ArXiv.org·2025· 1 citedOpen Access
Anharmonic infrared spectra of cationic pyrene and superhydrogenated derivatives
The Journal of Chemical Physics·2025· 1 citedOpen Access
Δ-model correction of foundation model based on the model’s own understanding
The Journal of Chemical Physics·2025· 7 citedOpen Access
Water Nucleation via Transient Bonds to Oxygen-Functionalized Graphite
The Journal of Physical Chemistry C·2025· 1 citedOpen Access
Augmentation of Universal Potentials for Broad Applications
Physical Review Letters·2025· 9 citedOpen Access
Generative diffusion model for surface structure discovery
Physical review. B./Physical review. B·2024· 19 cited
Efficient ensemble uncertainty estimation in Gaussian processes regression
Machine Learning Science and Technology·2024· 4 citedOpen Access
Molecular Hydrogen Formation via Vibrational Excitation of Partially Superhydrogenated Pyrenes
The Astrophysical Journal·2024· 2 citedOpen Access
Efficient ensemble uncertainty estimation in Gaussian Processes Regression
arXiv (Cornell University)·2024Open Access
Accelerating structure search using atomistic graph-based classifiers
The Journal of Chemical Physics·2024· 5 citedOpen Access
Cascading symmetry constraint during machine learning-enabled structural search for sulfur-induced Cu(111)-(43×43) surface reconstruction
The Journal of Chemical Physics·2024· 8 citedOpen Access
Generative diffusion model for surface structure discovery
arXiv (Cornell University)·2024· 2 citedOpen Access
Machine-learned search for the stable structures of silicene on Ag(111)
Physical Review Materials·2023· 4 citedOpen Access
Generating candidates in global optimization algorithms using complementary energy landscapes
The Journal of Chemical Physics·2023· 11 citedOpen Access
A machine learning potential for simulating infrared spectra of nanosilicate clusters
The Journal of Chemical Physics·2023· 15 citedOpen Access
A machine learning potential for simulating infrared spectra of nanosilicate clusters
arXiv (Cornell University)·2023Open Access
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Frequent Co-authors

Jens K. Nørskov(19), Flemming Besenbacher(13), L. M. Molina(9), Erik Lægsgaard(8), Željko Šljivančanin(5), Karsten W. Jacobsen(4), I. Stensgaard(4), Stefan Wendt(4), J. Matthiesen(4), L. B. Hansen(3), Jens Jørgen Mortensen(3), Liv Hornekær(3), Renald Schaub(3), M. D. Rasmussen(3), Trolle R. Linderoth(3), Roberto Otero(3), E. Rauls(3), Ask Hjorth Larsen(2), Marcin Dułak(2), Thomas Olsen(2)