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)
Max Planck scientists design a process that merges metal extraction, alloying and processing into one single, eco-friendly step. Their results are now published in the journal Nature.
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
While Density Functional Theory (DFT) is in principle exact, the exchange functional remains unknown, which limits the accuracy of DFT simulation. Still, in addition to the accuracy of the exchange functional, the quality of material properties calculated with DFT is also restricted by the choice of finite bases sets.
The Atom Probe Tomography group in the Microstructure Physics and Alloy Design department is developing integrated protocols for ultra-high vacuum cryogenic specimen transfer between platforms without exposure to atmospheric contamination.
Many important phenomena occurring in polycrystalline materials under large plastic strain, like microstructure, deformation localization and in-grain texture evolution can be predicted by high-resolution modeling of crystals. Unfortunately, the simulation mesh gets distorted during the deformation because of the heterogeneity of the plastic…
Here, we aim to develop machine-learning enhanced atom probe tomography approaches to reveal chemical short/long-range order (S/LRO) in a series of metallic materials.
Hydrogen embrittlement is one of the most substantial issues as we strive for a greener future by transitioning to a hydrogen-based economy. The mechanisms behind material degradation caused by hydrogen embrittlement are poorly understood owing to the elusive nature of hydrogen. Therefore, in the project "In situ Hydrogen Platform for…
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.