Publikationen von Marvin Poul

Poul, M.; Huber, L.; Neugebauer, J.: Automated generation of structure datasets for machine learning potentials and alloys. npj Computational Materials 11 (1), 174 (2025)

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)

Dsouza, R.; Poul, M.; Huber, L.; Swinburne, T. D.; Neugebauer, J.: Sampling-free computation of finite temperature material properties in isochoric and isobaric ensembles using the mean-field anharmonic bond model. Physical Review B 109, 064108 (2024)

Yilmaz, C.; Poul, M.; Lahn, L.; Raabe, D.; Zaefferer, S.: Dislocation-assisted particle dissolution: A new hypothesis for abnormal growth of Goss grains in grain-oriented electrical steels. Acta Materialia 258, 119170 (2023)

Poul, M.; Huber, L.; Bitzek, E.; Neugebauer, J.: Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium. Physical Review B 107, 104103 (2023)

Neugebauer, J.; Poul, M.; Mathews, P.; Tehranchi, A.; Yang, J.; Todorova, M.; Hickel, T.: Construction and application of defect phase diagrams: Concepts and computational approaches. Thermec 2023, Vienna, Austria (2023)

Neugebauer, J.; Poul, M.; Mathews, P.; Tehranchi, A.; Yang, J.; Todorova, M.; Hickel, T.: Defect phase diagrams: Concepts, computational approaches and applications. DPG-Frühjahrstagung (DPG Spring Meeting), Dresden, Germany (2023)

Poul, M.; Huber, L.; Bitzek, E.; Neugebauer, J.: Systematic Structure Datasets for Machine Learning Potentials: Application to Moment Tensor Potentials of Magnesium and its Defects. arXiv (2022)