Katnagallu, S.; Freysoldt, C.; Gault, B.; Neugebauer, J.: Ab initio vacancy formation energies and kinetics at metal surfaces under high electric field. Physical Review B 107 (4), L041406 (2023)

Khorrami, M. S.; Mianroodi, J. R.; Siboni, N. H.; Goyal, P. K.; Svendsen, B.; Benner, P.; Raabe, D.: An Artificial Neural Network for Surrogate Modeling of Stress Fields in Viscoplastic Polycrystalline Materials. npj Computational Materials 9, 37 (2023)

Kour, K.; Dolgov, S.; Stoll, M.; Benner, P.: Efficient Structure-preserving Support Tensor Train Machine. Journal of Machine Learning Research 24 (4), pp. 1 - 22 (2023)

Pincelli, T.; Vasileiadis, T.; Dong, S.; Beaulieu, S.; Dendzik, M. R.; Zahn, D.; Lee, S.-E.; Seiler, H.; Qi, Y.; Xian, R. P. et al.; Maklar, J.; Coy, E.; Mueller, N. S.; Okamura, Y.; Reich, S.; Wolf, M.; Rettig, L.; Ernstorfer, R.: Observation of Multi-Directional Energy Transfer in a Hybrid Plasmonic–Excitonic Nanostructure. Advanced Materials 35 (9), 2209100 (2023)

Xian, R. P.; Stimper, V.; Zacharias, M.; Dendzik, M. R.; Dong, S.; Beaulieu, S.; Schölkopf, B.; Wolf, M.; Rettig, L.; Carbogno, C. et al.; Bauer, S.; Ernstorfer, R.: A machine learning route between band mapping and band structure. Nature Computational Science 3 (1), pp. 101 - 114 (2023)

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)

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: Science and Technology 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, 20210883 (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 (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)

Andersen, C. W.; Armiento, R.; Blokhin, E.; Conduit, G. J.; Dwaraknath, S.; Evans, M. L.; Fekete, Á.; Gopakumar, A.; Gražulis, S.; Merkys, A. et al.; Mohamed, F. R.; Oses, C.; Pizzi, G.; Rignanese, G.-M.; Scheidgen, M.; Talirz, L.; Toher, C.; Winston, D.; Aversa, R.; Choudhary, K.; Colinet, P.; Curtarolo, S.; Di Stefano, D.; Draxl, C.; Er, S.; Esters, M.; Fornari, M.; Giantomassi, M.; Govoni, M.; Hautier, G.; Hegde, V.; Horton, M. K.; Huck, P.; Huhs, G.; Hummelshøj, J.; Kariryaa, A.; Kozinsky, B.; Kumbhar, S.; Liu, M.; Marzari, N.; Morris, A. J.; Mostofi, A. A.; Persson, K. A.; Petretto, G.; Purcell, T.; Ricci, F.; Rose, F.; Scheffler, M.; Speckhard, D.; Uhrin, M.; Vaitkus, A.; Villars, P.; Waroquiers, D.; Wolverton, C.; Wu, M.; Yang, X.: OPTIMADE, an API for exchanging materials data. Scientific Data 8, 217 (2021)

Cautaerts, N.; Rauch, E. F.; Jeong, J.; Dehm, G.; Liebscher, C.: Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. Scripta Materialia 201, 113930 (2021)

De Falco, P.; Weinkamer, R.; Wagermaier, W.; Li, C.; Snow, T.; Terrill, N. J.; Gupta, H. S.; Goyal, P.; Stoll, M.; Benner, P. et al.; Fratzl, P.: Tomographic X-ray scattering based on invariant reconstruction: analysis of the 3D nanostructure of bovine bone. Journal of Applied Crystallography 54 (2), pp. 486 - 497 (2021)

Dutta, A.; Vreeken, J.; Ghiringhelli, L. M.; Bereau, T.: Data-driven equation for drug-membrane permeability across drugs and membranes. The Journal of Chemical Physics 154 (24), 244114 (2021)

Evans, M. L.; Andersen, C. W.; Dwaraknath, S.; Scheidgen, M.; Fekete, Á.; Winston, D.: optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs. The Journal of Open Source Software 6 (65), 3458 (2021)

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, pp. 1 - 11 (2021)

Krynski, M.; Rossi, M.: Efficient Gaussian Process Regression for prediction of molecular crystals harmonic free energies. npj Computational Materials 7, 169 (2021)