Benavides-Riveros, C. L.; Chen, L.; Schilling, C.; Mantilla, S.; Pitallis, S.: Excitations of Quantum Many-Body Systems via Purified Ensembles: A Unitary-Coupled-Cluster-Based Approach. Physical Review Letters 129 (6), 066401 (2022)

Bowker, M.; DeBeer, S.; Dummer, N. F.; Hutchings, G. J.; Scheffler, M.; Schüth, F.; Taylor, S. H.; Tüysüz, H.: Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT). Angewandte Chemie International Edition 61 (50), e202209016 (2022)

Cautaerts, N.; Crout, P.; Ånes, H. W.; Prestat, E.; Jeong, J.; Dehm, G.; Liebscher, C.: Free, flexible and fast: Orientation mapping using the multi-core and GPU-accelerated template matching capabilities in the Python-based open source 4D-STEM analysis toolbox Pyxem. Ultramicroscopy 237, 113517 (2022)

Dutta, A.; Bereau, T.; Vilgis, T. A.: Identifying Sequential Residue Patterns in Bitter and Umami Peptides. ACS Food Science & Technology 2 (11), pp. 1773 - 1780 (2022)

Gault, B.; Klaes, B.; Morgado, F. F.; Freysoldt, C.; Li, Y.; De Geuser, F.; Stephenson, L.; Vurpillot, F.: Reflections on the Spatial Performance of Atom Probe Tomography in the Analysis of Atomic Neighborhoods. Microscopy and Microanalysis 28 (4), pp. 1116 - 1126 (2022)

Gedeon, J.; Schmidt, J.; Hodgson, M. J. P.; Wetherell, J.; Benavides-Riveros, C. L.; Marques, M. A. L.: Machine learning the derivative discontinuity of density-functional theory. Machine Learning 3 (1), 015011 (2022)

Goyal, P. K.; Benner, P.: Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach. Proceedings of the Royal Society A 478 (2262), 20210883 (2022)

Kuban, M.; Gabaj, Š.; Aggoune, W.; Vona, C.; Rigamonti, S.; Draxl, C.: Similarity of materials and data-quality assessment by fingerprinting. MRS Bulletin 47, pp. 991 - 999 (2022)

Kuban, M.; Rigamonti, S.; Scheidgen, M.; Draxl, C.: Density-of-states similarity descriptor for unsupervised learning from materials data. Scientific Data 9, 646 (2022)

Kühbach, M. T.; London, A. J.; Wang, J.; Schreiber, D. K.; Mendez Martin, F.; Ghamarian, I.; Bilal, H.; Ceguerra, A. V.: Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy. Microscopy and Microanalysis 28 (4), pp. 1038 - 1053 (2022)

Miyazaki, R.; Iida, K.; Ohno, S.; Matsuzaki, T.; Suzuki, T.; Arisawa, M.; Hasegawa, J.-y.: Substrate-Assisted Reductive Elimination Determining the Catalytic Cycle: A Theoretical Study on the Ni-Catalyzed 2,3-Disubstituted Benzofuran Synthesis via C-O Bond Activation. Organometallics 41 (23), pp. 3581 - 3588 (2022)

Peivaste, I.; Hamidi Siboni, N.; Alahyarizadeh, G.; Ghaderi, R.; Svendsen, B.; Raabe, D.; Mianroodi, J. R.: Machine-learning-based surrogate modeling of microstructure evolution using phase-field. Computational Materials Science 214, 111750 (2022)

Peng, Z.; Meiners, T.; Lu, Y.; Liebscher, C.; Kostka, A.; Raabe, D.; Gault, B.: Quantitative analysis of grain boundary diffusion, segregation and precipitation at a sub-nanometer scale. Acta Materialia 225, 117522 (2022)

Regler, B.; Scheffler, M.; Ghiringhelli, L. M.: TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions. Data Mining and Knowledge Discovery 36 (5), pp. 1815 - 1864 (2022)

Sasidhar, K. N.; Hamidi Siboni, N.; Mianroodi, J. R.; Rohwerder, M.; Neugebauer, J.; Raabe, D.: Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys. npj Materials Degradation 6 (1), 71 (2022)

Scheffler, M.; Aeschlimann, M.; Albrecht, M.; Bereau, T.; Bungartz, H.-J.; Felser, C.; Greiner, M.; Groß, A.; Koch, C. T.; Kremer, K. et al.; Nagel, W. E.; Scheidgen, M.; Wöll, C.; Draxl, C.: FAIR data enabling new horizons for materials research. Nature 604 (7907), pp. 635 - 642 (2022)