Publications of Jan Janßen

Janßen, J.; George, J.; Geiger, J.; Bercx, M.; Wang, X.; Ertural, C.; Schaarschmidt, J.; Ganose, A. M.; Pizzi, G.; Hickel, T. et al.; Neugebauer, J.: A python workflow definition for computational materials design. Digital Discovery 4, pp. 3149 - 3161 (2025)

Janßen, J.; Taylor, M.; Yang, P.; Neugebauer, J.; Perez, D.: Executorlib – Up-scaling Python workflows for hierarchical heterogenous high-performance computing. The Journal of Open Source Software (JOSS) 10 (108), 7782 (2025)

Zimmermann, Y.; Bazgir, A.; Al-Feghali, A.; Ansari, M.; Bocarsly, J.; Brinson, L. C.; Chiang, Y.; Circi, D.; Chiu, M.-H.; Daelman, N. et al.; Evans, M. L.; Gangan, A. S.; George, J.; Harb, H.; Khalighinejad, G.; Takrim Khan, S.; Klawohn, S.; Lederbauer, M.; Mahjoubi, S.; Mohr, B.; Mohamad Moosavi, S.; Naik, A.; Beste Ozhan, A.; Plessers, D.; Roy, A.; Schöppach, F.; Schwaller, P.; Terboven, C.; Ueltzen, K.; Wu, Y.; Zhu, S.; Janßen, J.; Li, C.; Foster, I.; Blaiszik, B.: 32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery. Machine Learning: Science and Technology 6 (3), 030701 (2025)

Janßen, J.; Makarov, E.; Hickel, T.; Shapeev, A. V.; Neugebauer, J.: Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations. npj Computational Materials 10 (1), 263 (2024)

Menon, S.; Lysogorskiy, Y.; Knoll, A. L. M.; Leimeroth, N.; Poul, M.; Qamar, M.; Janssen, J.; Mrovec, M.; Rohrer, J.; Albe, K. et al.; Behler, J.; Drautz, R.; Neugebauer, J.: From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows. npj Computational Materials 10 (1), 261 (2024)

Zhu, L.-F.; Janßen, J.; Ishibashi, S.; Körmann, F.; Grabowski, B.; Neugebauer, J.: A fully automated approach to calculate the melting temperature of elemental crystals. Computational Materials Science 187 (11), 110065 (2021)