Publications

Jan‐Niklas Eckardt, Waldemar Hahn, Rhonda E. Ries, Szymon D. Chrost, Susann Winter, Sebastian Stasik, Christoph Röllig, Uwe Platzbecker, Carsten Müller‐Tidow, Hubert Serve, Claudia D. Baldus, Christoph Schliemann, Kerstin Schäfer‐Eckart, Maher Hanoun, Martin Kaufmann, Andreas Burchert, Johannes Schetelig, Martin Bornhäuser, Markus Wolfien, Soheil Meshinchi, Christian Thiede, and Jan Moritz Middeke. Age‐stratified machine learning identifies divergent prognostic significance of molecular alterations in AML. HemaSphere, (9)5Wiley, May 2025. [PUMA: AML machine_learning yaff] URL

Christoph Lehmann, and Yahor Paromau. Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions. 2025. [PUMA: machine_learning quantifying uncertainty xack yaff] URL

Maximilian X. Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen, Julia Westermayr, Philipp Marquetand, Johannes C. B. Dietschreit, and Leticia González. Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials. Digital Discovery, (4)6:1478-1491, RSC, 2025. [PUMA: dynamics interatomic machine_learning nonadiabatic potentials yaff] URL

Rhyan Barrett, Christoph Ortner, and Julia Westermayr. Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets. 2025. [PUMA: DeepSets X-MACE machine_learning yaff] URL

Frank Cichos, Santiago Muiños Landin, and Ravi Pradip. Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter. In Yuebing Zheng, and Zilong Wu (Eds.), Intelligent Nanotechnology, 113-144, Elsevier, 2023. [PUMA: Active_particles Feedback_control Machine_learning Multi_agent_reinforcement_learning Optical_control Reinforcement_learning nopdf] URL

Julia Peters, Anja Neumann, Marco Jaeger, Lukas Gienapp, and Josefine Umlauft. ml4xcube: Machine Learning Toolkits for Earth System Data Cubes. 28302-28311, April 2025. [PUMA: Earth_System_Data_Cubes Machine_Learning ml4xcube yaff] URL

Badal Mondal, Julia Westermayr, and Ralf Tonner-Zech. Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors. The Journal of Chemical Physics, (159)10AIP Publishing, September 2023. [PUMA: Crystal_lattices Density_functional_theory First-principle_calculations Machine_learning Optoelectronic_application Optoelectronic_devices Regression_analysis Semiconductors Support_vector_machine yaff] URL

Toni Oestereich, Ralf Tonner‐Zech, and Julia Westermayr. Decoding energy decomposition analysis: Machine‐learned Insights on the impact of the density functional on the bonding analysis. Journal of Computational Chemistry, (45)7:368–376, Wiley, November 2023. [PUMA: chemical_bonding density_functional_theory energy_decomposition_analysis feature_importance_analysis machine_learning zno] URL

Moritz Ertelt, Rocco Moretti, Jens Meiler, and Clara T. Schoeder. Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants. Science Advances, (11)7:eadr7338, American Association for the Advancement of Science, June 2025. [PUMA: machine_learning protein_design topic_lifescience xack yaff]