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Diehl, M.; Kühbach, M.: Coupled experimental-computational analysis of primary static recrystallization in low carbon steel. Modelling and Simulation in Materials Science and Engineering 28 (1), 014001 (2019)

Draxl, C.; Scheffler, M.: The NOMAD Laboratory: From Data Sharing to Artificial Intelligence. JPhys Materials 2 (3), 036001 (2019)

Goldsmith, B.; Florian, J.; Liu, J.-X.; Grüne, P.; Lyon, J. T.; Rayner, D. M.; Fielicke, A.; Scheffler, M.; Ghiringhelli, L. M.: Two-to-three dimensional transition in neutral gold clusters: The crucial role of van der Waals interactions and temperature. Physical Review Materials 3 (1), 016002 (2019)

Janßen, J.; Surendralal, S.; Lysogorskiy, Y.; Todorova, M.; Hickel, T.; Drautz, R.; Neugebauer, J.: pyiron: An integrated development environment for computational materials sciences. Computational Materials Science 163, pp. 24 - 36 (2019)

Kapil, V.; Engel, E.; Rossi, M.; Ceriotti, M.: Assessment of Approximate Methods for Anharmonic Free Energies. Journal of Chemical Theory and Computation 15 (11), pp. 5845 - 5857 (2019)

Kontis, P.; Chauvet, E.; Peng, Z.; He, J.; Kwiatkowski da Silva, A.; Raabe, D.; Tassin, C.; Blandin, J.-J.; Abed, S.; Dendievel, R. et al.; Gault, B.; Martin, G.: Atomic-scale grain boundary engineering to overcome hot-cracking in additively-manufactured superalloys. Acta Materialia 177, pp. 209 - 221 (2019)

Kühbach, M.; Bajaj, P.; Breen, A. J.; Jägle, E. A.; Gault, B.: On Strong Scaling Open Source Tools for Mining Atom Probe Tomography Data. Microscopy and Microanalysis 25 (S2 ), pp. 298 - 299 (2019)

Kühbach, M.; Breen, A. J.; Herbig, M.; Gault, B.: Building a Library of Simulated Atom Probe Data for Different Crystal Structures and Tip Orientations Using TAPSim. Microscopy and Microanalysis 25 (2), pp. 320 - 330 (2019)

Lenz, M.-O.; Purcell, T.; Hicks, D.; Curtarolo, S.; Scheffler, M.; Carbogno, C.: Parametrically constrained geometry relaxations for high-throughput materials science. npj Computational Materials 5, 123 (2019)

Lysogorskiy, Y.; Hammerschmidt, T.; Janßen, J.; Neugebauer, J.; Drautz, R.: Transferability of interatomic potentials for molybdenum and silicon. Modelling and Simulation in Materials Science and Engineering 27 (2), 025007 (2019)

Ouyang, R.; Ahmetcik, E.; Carbogno, C.; Scheffler, M.; Ghiringhelli, L. M.: Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO. JPhys Materials 2 (2), 024002 (2019)

Pandey, P.; Sawant, A. K.; Nithin, B.; Peng, Z.; Makineni, S. K.; Gault, B.; Chattopadhyay, K.: On the effect of Re addition on microstructural evolution of a CoNi-based superalloy. Acta Materialia 168, pp. 37 - 51 (2019)

Peng, Z.; Lu, Y.; Hatzoglou, C.; Kwiatkowski da Silva, A.; Vurpillot, F.; Ponge, D.; Raabe, D.; Gault, B.: An Automated Computational Approach for Complete In-Plane Compositional Interface Analysis by Atom Probe Tomography. Microscopy and Microanalysis 25 (2), pp. 389 - 400 (2019)

Peng, Z.; Zanuttini, D.; Gervais, B.; Jacquet, E.; Blum, I.; Choi, P.-P.; Raabe, D.; Vurpillot, F.; Gault, B.: Unraveling the Metastability of C_n²⁺ (n = 2-4) Clusters. The Journal of Physical Chemistry Letters 10 (3), pp. 581 - 588 (2019)

Raimbault, N.; Grisafi, A.; Ceriotti, M.; Rossi, M.: Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals. New Journal of Physics 21 (10), 105001 (2019)

Schilling, C.; Mack, T.; Lickfett, S.; Sieste, S.; Ruggeri, F. S.; Šneideris, T.; Dutta, A.; Bereau, T.; Naraghi, R.; Sinske, D. et al.; Knowles, T. P. J.; Synatschke, C. V.; Weil, T.; Knöll, B.: Sequence-Optimized Peptide Nanofibers as Growth Stimulators for Regeneration of Peripheral Neurons. Advanced Functional Materials 29 (24), 1809112 (2019)

Stimper, V.; Bauer, S.; Ernstorfer, R.; Schölkopf, B.; Xian, R. P.: Multidimensional Contrast Limited Adaptive Histogram Equalization. IEEE Access 7, pp. 165437 - 165447 (2019)

Wei, Y.; Peng, Z.; Kühbach, M.; Breen, A. J.; Legros, M.; Larranaga, M.; Mompiou, F.; Gault, B.: 3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods. PLoS One 14 (11), e0225041 (2019)

Xian, R. P.; Rettig, L.; Ernstorfer, R.: Symmetry-guided nonrigid registration: The case for distortion correction in multidimensional photoemission spectroscopy. Ultramicroscopy 202, pp. 133 - 139 (2019)

Zheng, F.; Gao, X.; Eisfeld, A.: Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques. Physical Review Letters 123 (16), 163202 (2019)

Bentley, C.; Eisfeld, A.: Gaussian processes for choosing laser parameters for driven, dissipative Rydberg aggregates. Journal of Physics B 51 (20), 205003 (2018)

Budhathoki, K.; Vreeken, J.: Origo: Causal Inference by Compression. Knowledge and Information Systems 56 (2), pp. 285 - 307 (2018)

Draxl, C.; Scheffler, M.: NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin 43 (9), pp. 676 - 682 (2018)

Katnagallu, S.; Gault, B.; Grabowski, B.; Neugebauer, J.; Raabe, D.; Nematollahi, G. A.: Advanced data mining in field ion microscopy. Materials Characterization 146, pp. 307 - 318 (2018)