Johannes T. Margraf

According to our database, Johannes T. Margraf authored at least 7 papers between 2020 and 2026.

Collaborative distances:

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

2026

Perspective: Towards sustainable exploration of chemical spaces with machine learning.

Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich, Janine George, Luca Ghiringhelli, Alejandra Hinostroza Caldas, Veronika Jurásková, Hannes Kneiding, Yury Lysogorskiy, Johannes T. Margraf, Hanna Türk, O. Anatole von Lilienfeld, Milica Todorovic, Alexandre Tkatchenko, Mariana Rossi, Gianaurelio Cuniberti

CoRR, April, 2026

Design Space of Self-Consistent Electrostatic Machine Learning Interatomic Potentials.

William J. Baldwin, Ilyes Batatia, Martin Vondrák, Johannes T. Margraf, Gábor Csányi

CoRR, March, 2026

2025

Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline.

Mengnan Cui, Karsten Reuter, Johannes T. Margraf

Mach. Learn. Sci. Technol., 2025

2024

Neural graph distance embedding for molecular geometry generation.

Johannes T. Margraf

J. Comput. Chem., 2024

2022

How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?

Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf

Mach. Learn. Sci. Technol., December, 2022

Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model.

Carsten G. Staacke, Simon Wengert, Christian Kunkel, Gábor Csányi, Karsten Reuter, Johannes T. Margraf

Mach. Learn. Sci. Technol., 2022

2020

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.

Johannes Klicpera, Shankari Giri, Johannes T. Margraf, Stephan Günnemann

CoRR, 2020