Jan Hamaekers

14 papers Journal 13
YearRankTypeTitle / Venue / Authors
2026 J jnl
Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training.
CoRR
Rick Oerder, Jan Hamaekers
2026 J jnl
Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers.
CoRR
Sindu B. S., Jan Hamaekers
2025 J jnl
ConfRank+: Extending Conformer Ranking to Charged Molecules.
J. Chem. Inf. Model.
Rick Oerder, Christian Hölzer, Jan Hamaekers
2025 J jnl
Materium: An Autoregressive Approach for Material Generation.
CoRR
Niklas Dobberstein, Jan Hamaekers
2025 J jnl
Multi-fidelity learning for atomistic models via trainable data embeddings.
Mach. Learn. Sci. Technol.
Rick Oerder, Gerrit Schmieden, Jan Hamaekers
2024 J jnl
ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training.
J. Chem. Inf. Model.
Christian Hölzer, Rick Oerder, Stefan Grimme, Jan Hamaekers
2024 J jnl
Llamol: a dynamic multi-conditional generative transformer for de novo molecular design.
J. Cheminformatics
Niklas Dobberstein, Astrid Maass, Jan Hamaekers
2024 J jnl
On Multilevel Energy-Based Fragmentation Methods.
CoRR
James Barker, Michael Griebel, Jan Hamaekers
2023 J jnl
LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design.
CoRR
Niklas Dobberstein, Astrid Maass, Jan Hamaekers
2023 J jnl
On the Interplay of Subset Selection and Informed Graph Neural Networks.
CoRR
Niklas Breustedt, Paolo Climaco, Jochen Garcke, Jan Hamaekers, Gitta Kutyniok, Dirk A. Lorenz, Rick Oerder, Chirag Varun Shukla
2023 J jnl
Parameterized Neural Networks for Finance.
CoRR
Daniel Oeltz, Jan Hamaekers, Kay F. Pilz
2021 J jnl
Similarity of particle systems using an invariant root mean square deviation measure.
CoRR
Johannes Bulin, Jan Hamaekers
2018 J jnl
An Adaptive Multiscale Approach for Electronic Structure Methods.
Multiscale Model. Simul.
Sambasiva Rao Chinnamsetty, Michael Griebel, Jan Hamaekers
2017 — ch.
LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential.
Scientific Computing and Algorithms in Industrial Simulations
James Barker, Johannes Bulin, Jan Hamaekers, Sonja Mathias