Zhu, L.-F.; Körmann, F.; Chen, Q.; Selleby, M.; Neugebauer, J.; Grabowski, B.: Accelerating ab initio melting property calculations with machine learning: application to the high entropy alloy TaVCrW. npj Computational Materials 10 (1), 274 (2024)
Zhu, L.-F.; Körmann, F.; Ruban, A. V.; Neugebauer, J.; Grabowski, B.: Performance of the standard exchange-correlation functionals in predicting melting properties fully from first principles: Application to Al and magnetic Ni. Physical Review B 101 (14), 144108 (2020)
Zhu, L.-F.; Grabowski, B.; Neugebauer, J.: Efficient approach to compute melting properties fully from ab initio with application to Cu. Physical Review B 96 (22), 224202 (2017)
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)
Zhu, L.-F.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential. The third generation (3G) Calphad at KTH, Stockholm, Sweden (2023)
Zhu, L.-F.; Neugebauer, J.; Grabowski, B.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential. CALPHAD L Conference, Cambridge, MA, USA (2023)
Zhu, L.-F.: Melting properties from ab initio using efficient TOR-TILD approach: Applications to refractory metals V, W and V–W alloy. CALPHAD XLVIII Conference, Stockholm, Sweden (2023)
Zhu, L.-F.: Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential and pyiron workflow. CM retreat, Ebernburg, Germany (2022)
Efficient harvesting of sunlight and (photo-)electrochemical conversion into solar fuels is an emerging energy technology with enormous promise. Such emerging technologies depend critically on materials systems, in which the integration of dissimilar components and the internal interfaces that arise between them determine the functionality.
Laser Powder Bed Fusion (LPBF) is the most commonly used Additive Manufacturing processes. One of its biggest advantages it offers is to exploit its inherent specific process characteristics, namely the decoupling the solidification rate from the parts´volume, for novel materials with superior physical and mechanical properties. One prominet…
In this project we study - together with the department of Prof. Neugebauer and Dr. Sandlöbes at RWTH Aachen - the underlying mechanisms that are responsible for the improved room-temperature ductility in Mg–Y alloys compared to pure Mg.