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Babushkin, I., Galán, Á. J., de Andrade, J. R. C., Husakou, A., Morales, F., Kretschmar, M., Nagy, T., Vaičaitis, V., Shi, L., Zuber, D., Bergé, L., Skupin, S., Nikolaeva, I. A., Panov, N. A., Shipilo, D. E., Kosareva, O. G., Pfeiffer, A. N., Demircan, A., Vrakking, M. J. J., ... Ivanov, M. (2022). All-optical attoclock for imaging tunnelling wavepackets. Nature physics, 18(4), 417-422. doi.org/10.48550/arXiv.1803.04187, doi.org/10.1038/s41567-022-01505-2
Babushkin, I., Demircan, A., Kues, M., & Morgner, U. (2022). Wave-Shape-Tolerant Photonic Quantum Gates. Physical Review Letters, 128(9), [090502]. doi.org/10.1103/PhysRevLett.128.090502
Brunotte, W., Nagel, L., Schneider, K., & Klünder, J. A-C. (2022). How to Identify Changing Contexts of Use with Creativity Workshops: An Experience Report. in C. Ardito, R. Lanzilotti, A. Malizia, M. Larusdottir, L. D. Spano, J. Campos, M. Hertzum, T. Mentler, J. Abdelnour Nocera, L. Piccolo, S. Sauer, & G. van der Veer (Hrsg.), Sense, Feel, Design: INTERACT 2021 IFIP TC 13 Workshops, Revised Selected Papers (S. 88-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13198 LNCS). Springer Nature Switzerland AG. doi.org/10.1007/978-3-030-98388-8_9
Brunotte, W., Chazette, L., Klös, V., & Speith, T. (2022). Quo Vadis, Explainability? A Research Roadmap for Explainability Engineering. in V. Gervasi, & A. Vogelsang (Hrsg.), Requirements Engineering: Foundation for Software Quality : 28th International Working Conference, REFSQ 2022, Birmingham, UK, March 21–24, 2022, Proceedings (S. 26-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13216 LNCS). Springer Nature Switzerland AG. doi.org/10.1007/978-3-030-98464-9_3
Brunotte, W., Chazette, L., Köhler, L., Klünder, J. A-C., & Schneider, K. (2022). What About My Privacy? Helping Users Understand Online Privacy Policies. in Proceedings - 2022 IEEE/ACM Joint 16th International Conferenceon Software and System Processes and 17th ACM/IEEE International Conference on Global Software Engineering, ICSSP/ICGSE 2022: ICSSP'22 (S. 56–65). (ACM International Conference Proceeding Series). ACM DL. doi.org/10.1145/3529320.3529327
He, L., Guo, HW., Jin, Y., Zhuang, XY., Rabczuk, T., & Li, Y. (2022). Machine-learning-driven on-demand design of phononic beams. Science China: Physics, Mechanics and Astronomy, 65(1), [214612]. doi.org/10.1007/s11433-021-1787-x
Heinemann, D., Zabic, M., Terakawa, M., & Boch, J. (2022). Laser-based molecular delivery and its applications in plant science. Plant Methods, 18(1), [82]. doi.org/10.1186/s13007-022-00908-9
Herrmann, T., Schierz, A. K., Prediger, M., Reifenrath, J., Meißner, J., Wurz, M. C., & Behrens, P. (2022). Effect of PEG functionalization on the saturation magnetization of magnetic nanoporous core-shell nanoparticles. International Journal on Magnetic Particle Imaging, 8(1), [2203009]. doi.org/10.18416/ijmpi.2022.2203009
Jin, Y., He, L., Wen, Z., Mortazavi, B., Guo, HW., Torrent, D., Djafari-Rouhani, B., Rabczuk, T., Zhuang, XY., & Li, Y. (2022). Intelligent on-demand design of phononic metamaterials. Nanophotonics, 11(3), 439-460. doi.org/10.1515/nanoph-2021-0639
Keppler, N. C., Josephine Hindricks, K. D., & Behrens, P. (2022). Large refractive index changes in ZIF-8 thin films of optical quality. RSC Advances, 12(10), 5807-5815. doi.org/10.1039/d1ra08531j
Klepzig, L. F., Biesterfeld, L., Romain, M., Niebur, A., Schlosser, A., Hübner, J., & Lauth, J. (2022). Colloidal 2D PbSe nanoplatelets with efficient emission reaching the telecom O-, E- and S-band. Nanoscale Advances, 4(2), 590-599. doi.org/10.1039/d1na00704a
Leffers, L., Roth, B., & Overmeyer, L. (2022). Polymer optical Bend Sensor based on eccentric Fiber Bragg Gratings for 3D Shape Detection. in W. M. Shensky, I. Rau, & O. Sugihara (Hrsg.), Organic Photonic Materials and Devices XXIV [1199808] (Proceedings of SPIE - The International Society for Optical Engineering; Band 11998). SPIE. doi.org/10.1117/12.2609764
Liu, B., Vu-Bac, N., Fu, X., Zhuang, X., & Rabczuk, T. (2022). Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach. Composite structures, 289, [115393]. doi.org/10.1016/j.compstruct.2022.115393
Malek, T., Winkler, M., Denkena, B., & Wichmann, M. (2022). Energieeffiziente Prozessplanung. VDI-Z Integrierte Produktion, 164(4), 38-41. doi.org/10.37544/0042-1766-2022-04-38
Melchert, O., & Demircan, A. (2022). py-fmas: A python package for ultrashort optical pulse propagation in terms of forward models for the analytic signal. Computer Physics Communications, 273, [108257]. doi.org/10.1016/j.cpc.2021.108257
Mortazavi, B., Shojaei, F., Shapeev, A. V., & Zhuang, X. (2022). A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides. CARBON, 194, 230-239. doi.org/10.1016/j.carbon.2022.03.068
Mortazavi, B., Shahrokhi, M., Shojaei, F., Rabczuk, T., Zhuang, X., & Shapeev, A. V. (2022). A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C5N monolayers. NANOSCALE, 14(11), 4324-4333. doi.org/10.1039/d1nr06449e
Mortazavi, B., Novikov, I. S., & Shapeev, A. V. (2022). A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers. CARBON, 188, 431-441. doi.org/10.1016/j.carbon.2021.12.039
Mortazavi, B., Rajabpour, A., Zhuang, XY., Rabczuk, T., & Shapeev, AV. (2022). Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials. CARBON, 186, 501-508. doi.org/10.1016/j.carbon.2021.10.059
