Publications of Simon Wengert

Grigorev, P.; Frérot, L.; Birks, F.; Gola, A.; Golebiowski, J.; Grießer, J.; Hörmann, J. L.; Klemenz, A.; Moras, G.; Nöhring, W. G. et al.; Oldenstaedt, J. A.; Patel, P.; Reichenbach, T.; Shenoy, L.; Walter, M.; Wengert, S.; Kermode, J. R.; Pastewka, L.: matscipy: materials science at the atomic scale with Python. The Journal of Open Source Software (JOSS) 9 (93), 5668 (2024)

Gelžinytė, E.; Wengert, S.; Stenczel, T. K.; Heenen, H.; Reuter, K.; Csányi, G.; Bernstein, N.: wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows. The Journal of Chemical Physics 159 (12), 124801 (2023)

Staacke, C.; Wengert, S.; Kunkel, C.; Csányi, G.; Reuter, K.; Margraf, J.: Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model. Machine Learning: Science and Technology 3 (1), 015032 (2022)

Wengert, S.; Csányi, G.; Reuter, K.; Margraf, J.: A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings. Journal of Chemical Theory and Computation 18 (7), pp. 4586 - 4593 (2022)

Stegmaier, S.; Schierholz, R.; Povstugar, I.; Barthel, J.; Rittmeyer, S. P.; Yu, S.; Wengert, S.; Rostami, S.; Kungl, H.; Reuter, K. et al.; Eichel, R.-A.; Scheurer, C.: Nano-Scale Complexions Facilitate Li Dendrite-Free Operation in LATP Solid-State Electrolyte. Advanced Energy Materials 11 (26), 2100707 (2021)

Wengert, S.; Kunkel, C.; Margraf, J.; Reuter, K.: Accelerating molecular materials discovery with machine-learning. In: High-Performance Computing and Data Science in the Max Planck Society, pp. 40 - 41. Max Planck Computing and Data Facility, Garching (2021)

Wengert, S.: Kernel-based machine learning for molecular crystal structure prediction. Dissertation, Technische Universität, München (2022)