Neugebauer, J.: Fundamental compositional limitations in the thin film growth of metastable alloys. 3rd Conference on Advanced Functional Materials (AFM2018), Vildmarkshotellet Kolmården, Norrköping, Sweden (2018)
Neugebauer, J.: Modelling thermodynamics and kinetics of general grain boundaries: Challenges and successes. Thermec 2018 Conference, Paris, France (2018)
Neugebauer, J.: First-principles approaches for charged defects in low dimensional systems. Conference on Physics of Defects in Solids, Trieste, Italy (2018)
Neugebauer, J.: Understanding fundamental doping and stoichiometry limits in semiconductors by ab initio modelling. EDS 2018 Conference, Thessaloniki, Greece (2018)
Zhu, L.-F.; Grabowski, B.; Neugebauer, J.: Efficient approach to compute melting properties fully from ab initio with application to Cu. CALPHAD XLVII Conference, Querétaro, México (2018)
Neugebauer, J.: Machine learning as tool to enhance ab initio based alloy design. Workshop: “Machine learning and data analytics in advanced metals processing", Manchester, UK (2018)
Neugebauer, J.: From electrons to the design of structurally complex materials. SFB ViCoM conference EPT 2018: From electrons to phase transitions, Vienna, Austria (2018)
Neugebauer, J.: Exploration of Large Ab Initio Data Spaces to Design Structural Materials with Superior Mechanical Properties. Hume-Rothery Award Symposium, TMS 2018, Phoenix, AZ, USA (2018)
Neugebauer, J.: Understanding the fundamental mechanisms behind H embrittlement: An ab initio guided multiscale approach. Seminar E2M ("Wall Forum") at MPI for Plasma Physics, Garching, Germany (2018)
Neugebauer, J.: A first principles approach to model electrochemical reactions in an electrolytic cell. Workshop: The Electrode Potential in Electrochemistry - A Challenge for Electronic Structure Theory Calculations, Schloß Reisensburg, Günzburg, Germany (2017)
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)
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…
Recent developments in experimental techniques and computer simulations provided the basis to achieve many of the breakthroughs in understanding materials down to the atomic scale. While extremely powerful, these techniques produce more and more complex data, forcing all departments to develop advanced data management and analysis tools as well as…