Researcher Portfolio
Dr. Wengert, Simon
Theory, Fritz Haber Institute, Max Planck Society
Researcher Profile
Position: Theory, Fritz Haber Institute, Max Planck Society
Additional IDs: ORCID:
https://orcid.org/0000-0002-8008-1482
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons261884
Publications
: Grigorev, P., Frérot, L., Birks, F., Gola, A., Golebiowski, J., Grießer, J., Hörmann, J. L., Klemenz, A., Moras, G., Nöhring, W. G., Oldenstaedt, J. A., Patel, P., Reichenbach, T., Shenoy, L., Walter, M., Wengert, S., Kermode, J. R., & Pastewka, L. (2024). matscipy: materials science at the atomic scale with Python. The Journal of Open Source Software (JOSS), 9(93): 5668. Retrieved from https://joss.theoj.org/papers/33f6a17885367fe629c3a73f27743945. [PubMan] : Gelžinytė, E., Wengert, S., Stenczel, T. K., Heenen, H., Reuter, K., Csányi, G., & Bernstein, N. (2023). wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows. The Journal of Chemical Physics, 159(12): 124801. doi:10.1063/5.0156845. [PubMan] : Wengert, S. (2022). Kernel-based machine learning for molecular crystal structure prediction. PhD Thesis, Technische Universität, München. [PubMan] : Wengert, S., Csányi, G., Reuter, K., & Margraf, J. (2022). A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings. Journal of Chemical Theory and Computation, 18(7), 4586-4593. doi:10.1021/acs.jctc.2c00343. [PubMan] : Staacke, C., Wengert, S., Kunkel, C., Csányi, G., Reuter, K., & Margraf, J. (2022). 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. doi:10.1088/2632-2153/ac568d. [PubMan] : Stegmaier, S., Schierholz, R., Povstugar, I., Barthel, J., Rittmeyer, S. P., Yu, S., Wengert, S., Rostami, S., Kungl, H., Reuter, K., Eichel, R.-A., & Scheurer, C. (2021). Nano-Scale Complexions Facilitate Li Dendrite-Free Operation in LATP Solid-State Electrolyte. Advanced Energy Materials, 11(26): 2100707. doi:10.1002/aenm.202100707. [PubMan] : Wengert, S., Kunkel, C., Margraf, J., & Reuter, K. (2021). Accelerating molecular materials discovery with machine-learning. In High-Performance Computing and Data Science in the Max Planck Society (pp. 40-41). Garching: Max Planck Computing and Data Facility. Retrieved from https://www.mpcdf.mpg.de/MPCDF_Brochure_2021. [PubMan]