Jan Hamaekers

According to our database, Jan Hamaekers authored at least 13 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers.

Sindu B. S., Jan Hamaekers

CoRR, March, 2026

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training.

Rick Oerder, Jan Hamaekers

CoRR, March, 2026

2025

Materium: An Autoregressive Approach for Material Generation.

Niklas Dobberstein, Jan Hamaekers

CoRR, December, 2025

Multi-fidelity learning for atomistic models via trainable data embeddings.

Rick Oerder, Gerrit Schmieden, Jan Hamaekers

Mach. Learn. Sci. Technol., 2025

ConfRank+: Extending Conformer Ranking to Charged Molecules.

Rick Oerder, Christian Hölzer, Jan Hamaekers

J. Chem. Inf. Model., 2025

2024

Llamol: a dynamic multi-conditional generative transformer for de novo molecular design.

Niklas Dobberstein, Astrid Maass, Jan Hamaekers

J. Cheminformatics, December, 2024

ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training.

Christian Hölzer, Rick Oerder, Stefan Grimme, Jan Hamaekers

J. Chem. Inf. Model., 2024

On Multilevel Energy-Based Fragmentation Methods.

James Barker, Michael Griebel, Jan Hamaekers

CoRR, 2024

2023

On the Interplay of Subset Selection and Informed Graph Neural Networks.

Niklas Breustedt, Paolo Climaco, Jochen Garcke, Jan Hamaekers, Gitta Kutyniok, Dirk A. Lorenz, Rick Oerder, Chirag Varun Shukla

CoRR, 2023

Parameterized Neural Networks for Finance.

Daniel Oeltz, Jan Hamaekers, Kay F. Pilz

CoRR, 2023

2021

Similarity of particle systems using an invariant root mean square deviation measure.

Johannes Bulin, Jan Hamaekers

CoRR, 2021

2018

An Adaptive Multiscale Approach for Electronic Structure Methods.

Sambasiva Rao Chinnamsetty, Michael Griebel, Jan Hamaekers

Multiscale Model. Simul., 2018

2017

LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential.

James Barker, Johannes Bulin, Jan Hamaekers, Sonja Mathias

Proceedings of the Scientific Computing and Algorithms in Industrial Simulations, 2017