Dutta, B.; Körmann, F.; Hickel, T.; Neugebauer, J.: Temperature-driven effects in functional materials: Ab initio insights. Talk at University Pierre and Marie CURIE (UPMC), Paris, France (2017)
Neugebauer, J.: Free energy sampling strategies for structurally complex materials. Workshop II: Stochastic Sampling and Accelerated Time Dynamics on Multidimensional Surfaces, IPAM, UCLA, Los Angeles, CA, USA (2017)
Dutta, B.; Olsen, R. J.; Mu, S.; Hickel, T.; Samolyuk, G. D.; Specht, E. D.; Bei, H.; Lindsay, L. R.; Neugebauer, J.; Stocks , M.et al.; Larson, B. C.: Lattice dynamics in high entropy alloys: understanding the role of fluctuations. EUROMAT 2017, Thessaloniki, Greece (2017)
Zhu, L.-F.; Grabowski, B.; Neugebauer, J.: Efficient approach to compute melting properties fully from ab initio with application to Cu. MPIE-ICAMS workshop, Ebernburg, Germany (2017)
Dey, P.; Yao, M.; Friák, M.; Hickel, T.; Raabe, D.; Neugebauer, J.: Ab-initio investigation of the role of kappa carbide in upgrading Fe–Mn–Al–C alloy to the class of advanced high-strength steels. ArcelorMittal Global R&D Gent, Thessaloniki, Greece (2017)
Neugebauer, J.: Fundamental compositional limitations in the thin film growth of metastable alloys. Rapidly Quenched & Metastable Materials 16, Leoben, Austria (2017)
Dutta, B.; Hickel, T.; Neugebauer, J.: Finite temperature excitation mechanisms and their coupling in magnetic shape memory alloys. The Materials Research Centre (MRC), Indian Institute of Science (IISc), Bangalore, India (2017)
Neugebauer, J.: From Semiconductors to High-Strength Steels and Back Again. 10 years of the Laboratory for Photovoltaics & Semiconductor Physics, Luxembourg, Luxembourg (2017)
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
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…