Li, Y.; Wei, Y.; Saxena, A.; Kühbach, M.; Freysoldt, C.; Gault, B.: Machine learning enhanced atom probe tomography analysis. Progress in Materials Science 156, 101561 (2026)

Eibl, S.; Yao, Y.; Scheffler, M.; Rampp, M.; Ghiringhelli, L. M.; Purcell, T.: A high-performance and portable implementation of the SISSO method for CPUs and GPUs. Machine Learning: Science and Technology 6 (4), 047001 (2025)

Bauer, S.; Benner, P.; Bereau, T.; Blum, V.; Boley, M.; Carbogno, C.; A. Catlow, C. R.; Dehm, G.; Eibl, S.; Ernstorfer, R. et al.; Fekete, Á.; Foppa, L.; Fratzl, P.; Freysoldt, C.; Gault, B.; M. Ghiringhelli, L.; Sajal, K. G.; Gladyshev, A.; Goyal, P. K.; Hattrick-Simpers, J.; Kabalan, L.; Karpov, P.; S. Khorrami, M.; Koch, C.; Kokott, S.; Kosch, T.; Kowalec, I.; Kremer, K.; Leitherer, A.; Li, Y.; Liebscher, C. H.; Logsdail, A. J.; Lu, Z.; Luong, F.; Marek, A.; Merz, F.; R. Mianroodi, J.; Neugebauer, J.; Purcell, T. A. R.; Raabe, D.; Rampp, M.; Rossi, M.; Rost, J.-M.; Saalmann, U.; Saxena, A.; Sbailo, L.; Scheffler, M.; Scheidgen, M.; Schloz, M.; Schmidt, D. F.; Teshuva, S.; Trunschke, A.; Wei, Y.; Weikum, G.; Xian, R. P.; Yao, Y.; Zhao, M.: Roadmap on Data-Centric Materials Science. Modelling and Simulation in Materials Science and Engineering 32, 063301,90 pages (2024)

Li, Y.; Colnaghi, T.; Gong, Y.; Zhang, H.; Yu, Y.; Wei, Y.; Gan, B.; Song, M.; Marek, A.; Rampp, M. et al.; Zhang, S.; Pei, Z.; Wuttig, M.; Ghosh, S.; Körmann, F.; Neugebauer, J.; Wang, Z.; Gault, B.: Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering. Advanced Materials 36 (44), 2407564 (2024)

Li, Y.; Gault, B.: Machine Learning Enhanced Tomographic Imaging of Chemical Short-range Order in Fe-based Solid Solutions. Microscopy and Microanalysis 30 (Supplement_1), pp. 44 - 45 (2024)

Miyazaki, R.; Belthle, K. S.; Tüysüz, H.; Foppa, L.; Scheffler, M.: Materials Genes of CO₂ Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data. Journal of the American Chemical Society 146 (8), pp. 5433 - 5444 (2024)

Miyazaki, R.; Faraji, S.; Levchenko, S. V.; Foppa, L.; Scheffler, M.: Vibrational frequencies utilized for the assessment of exchange-correlation functionals in the description of metal-adsorbate systems: C₂H₂ and C₂H₄ on transition-metal surfaces. Catalysis Science & Technology 14 (23), pp. 6924 - 6933 (2024)

Yin, J.; Rao, Z.; Wu, D.; Lv, H.; Ma, H.; Long, T.; Kang, J.; Wang, Q.; Wang, Y.; Su, R.: Interpretable Predicting Creep Rupture Life of Superalloys: Enhanced by Domain-Specific Knowledge. Advanced Science 11 (11), 2307982 (2024)

Yu, J.; Wang, Z.; Saksena, A.; Wei, S.; Wei, Y.; Colnaghi, T.; Marek, A.; Rampp, M.; Song, M.; Gault, B. et al.; Li, Y.: 3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures. Acta Materialia 278, 120280 (2024)

Duc Pham, L. V.; Sattler, P.; Marques, M. A. L.; Benavides-Riveros, C. L.: Homogeneous electron liquid in arbitrary dimensions beyond the random phase approximation. New Journal of Physics 25, 083040 (2023)

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)

Kaygisiz, K.; Dutta, A.; Rauch-Wirth, L.; Synatschke, C. V.; Münch, J.; Bereau, T.; Weil, T.: Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings. Biomaterials Science 11 (15), pp. 5251 - 5261 (2023)

Kaygisiz, K.; Rauch-Wirth, L.; Dutta, A.; Yu, X.; Nagata, Y.; Bereau, T.; Münch, J.; Synatschke, C. V.; Weil, T.: Data-mining unveils structure-property-activity correlation of viral infectivity enhancing self-assembling peptides. Nature Communications 14, 5121 (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)

Knoop, F.; Purcell, T.; Scheffler, M.; Carbogno, C.: Anharmonicity in Thermal Insulators: An Analysis from First Principles. Physical Review Letters 130 (23), 236301 (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)

Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. International Journal of Plasticity 171, 103776 (2023)

Leitherer, A.; Yeo, B. C.; Liebscher, C. H.; Ghiringhelli, L. M.: Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy. npj Computational Materials 9, 179 (2023)

Li, Y.; Wei, Y.; Wang, Z.; Liu, X.; Colnaghi, T.; Han, L.; Rao, Z.; Zhou, X.; Huber, L.; Dsouza, R. et al.; Gong, Y.; Neugebauer, J.; Marek, A.; Rampp, M.; Bauer, S.; Li, H.; Baker, I.; Stephenson, L.; Gault, B.: Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography. Nature Communications 14 (1), 7410 (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)

Purcell, T.; Scheffler, M.; Ghiringhelli, L. M.: Recent advances in the SISSO method and their implementation in the SISSO++ Code. The Journal of Chemical Physics 159 (11), 114110 (2023)

Rao, Z.; Li, Y.; Zhang, H.; Colnaghi, T.; Marek, A.; Rampp, M.; Gault, B.: Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms. Scripta Materialia 234, 115542 (2023)

Saxena, A.; Polin, N.; Kusampudi, N.; Katnagallu, S.; Molina-Luna, L.; Gutfleisch, O.; Berkels, B.; Gault, B.; Neugebauer, J.; Freysoldt, C.: A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data. Microscopy and Microanalysis 29 (5), pp. 1658 - 1670 (2023)

Scheidgen, M.; Himanen , L.; Ladines, A. N.; Sikter, D.; Nakhaee, M.; Fekete, A.; Chang, T.; Golparvar, A.; Márquez, J. A.; Brockhauser, S. et al.; Brückner , S.; Ghiringhelli, L. M.; Dietrich, F.; Lehmberg, D.; Denell, T.; Albino, A.; Näsström, H.; Shabih, S.; Dobener, F.; Kühbach, M.; Mozumder, R.; Rudzinski, J. F.; Daelman, N.; Pizarro, J. M.; Kuban, M.; Salazar, C.; Ondračka, P.; Bungartz, H.-J.; Draxl, C.: NOMAD: A distributed web-based platform for managing materials science research data. The Journal of Open Source Software (JOSS) 8 (90), 5388 (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)