Publications of Simon Wengert

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

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

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

Staacke, C., S. Wengert, C. Kunkel, G. Csányi, K. Reuter and J. Margraf: 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).

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

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

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