Pei, Z.; Gong, Y.; Singh, P.; Li, Y.; Körmann, F.; Xie, Q.; Wang, K.; Wu, X.; Mu, S.; Gao, M. C.et al.; Liaw, P. K.; Tong, Y.; Zhang, F.; Wang, Y.; Li, R.: Can chemical short-range order be transformed into a practical alloy-engineering tool? Current Opinion in Solid State and Materials Science 41, 101254 (2026)
Pei, Z.; Yin, J.; Neugebauer, J.; Jain, A.: Towards the holistic design of alloys with large language models. Nature Reviews Materials 9 (12), pp. 840 - 841 (2024)
Pei, Z.; Yin, J.; Liaw, P. K.; Raabe, D.: Author Correction: Toward the design of ultrahigh-entropy alloys via mining six million texts. Nature Communications 14 (1), 3588 (2023)
Pei, Z.; Yin, J.; Liaw, P. K.; Raabe, D.: Toward the design of ultrahigh-entropy alloys via mining six million texts. Nature Communications 14, 54 (2023)
Pei, Z.; Zhang, S.; Lei, Y.; Zhang, F.; Chen, M.: Decoupling between Shockley partials and stacking faults strengthens multiprincipal element alloys. Proceedings of the National Academy of Sciences of the United States of America 118 (51), e2114167118 (2021)
Pei, Z.; Stocks, G. M.: Origin of the sensitivity in modeling the glide behaviour of dislocations. International Journal of Plasticity 106, pp. 48 - 56 (2018)
Sandlöbes, S.; Friák, M.; Dick, A.; Zaefferer, S.; Yi, S.; Letzig, D.; Pei, Z.; Zhu, L.-F.; Neugebauer, J.; Raabe, D.: Complementary TEM and ab ignition study on the ductilizing effect of Y in solid solution Mg–Y alloys. In: Proceedings of the 9th Intern. Conference on Magnesium alloys and their applications, pp. 467 - 472. 9th Intern. Conference on Magnesium alloys and their applications, Vancouver, Canada, July 08, 2012 - July 12, 2012. (2012)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.