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
Crystal plasticity modelling has gained considerable momentum in the past 20 years [1]. Developing this field from its original mean-field homogenization approach using viscoplastic constitutive hardening rules into an advanced multi-physics continuum field solution strategy requires a long-term initiative. The group “Theory and Simulation” of…
The project Hydrogen Embrittlement Protection Coating (HEPCO) addresses the critical aspects of hydrogen permeation and embrittlement by developing novel strategies for coating and characterizing hydrogen permeation barrier layers for valves and pumps used for hydrogen storage and transport applications.
The project focuses on development and design of workflows, which enable advanced processing and analyses of various data obtained from different field ion emission microscope techniques such as field ion microscope (FIM), atom probe tomography (APT), electronic FIM (e-FIM) and time of flight enabled FIM (tof-FIM).
This project will aim at addressing the specific knowledge gap of experimental data on the mechanical behavior of microscale samples at ultra-short-time scales by the development of testing platforms capable of conducting quantitative micromechanical testing under extreme strain rates upto 10000/s and beyond.
The prediction of materials properties with ab initio based methods is a highly successful strategy in materials science. While the working horse density functional theory (DFT) was originally designed to describe the performance of materials in the ground state, the extension of these methods to finite temperatures has seen remarkable…
This work led so far to several high impact publications: for the first time nanobeam diffraction (NBD) orientation mapping was used on atom probe tips, thereby enabling the high throughput characterization of grain boundary segregation as well as the crystallographic identification of phases.