Vatti, A. K.; Todorova, M.; Neugebauer, J.: Formation Energy of Halide ions (Cl/Br/I) in water from ab-initio Molecular Dyna. Psi-k 2015 Conference, San Sebastián, Spain (2015)
Neugebauer, J.: Quantum-mechanical approaches to address the structural and thermodynamic complexity of engineering materials. Swedish Chemical Society, Kalmar, Sweden (2015)
Neugebauer, J.: Understanding the fundamental mechanisms behind H embrittlement: An ab initio guided multiscale approach. Colloquium UCB Vancouver, Vancouver, Canada (2015)
Neugebauer, J.: Vacancies in fcc metals: Discovery of large non-Arrhenius effects. The 5th Sino-German Symposium Thermodynamics and Kinetics of Nano and Mesoscale Materials and Their Applications, Changchun, China (2015)
Neugebauer, J.: Ab initio thermodynamics: A novel route to design materials on the computer. Colloquium at Universität Marburg, Marburg, Germany (2015)
Neugebauer, J.: Understanding the fundamental mechanisms behind H embrittlement: An ab initio guided multiscale approach. International Workshop MoD-PMI , Marseille, France (2015)
Neugebauer, J.: Materials design based on predictive ab initio thermodynamics. Colloquium at Lawrence Livermore National Lab, Livermore, CA, USA (2015)
Dutta, B.; Körmann, F.; Hickel, T.; Ghosh, S.; Sanyal, B.; Neugebauer, J.: The Itinerant Coherent Potential Approximation for phonons: role of fluctuations for systems with magnetic and chemical disorder. Materials Theory Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA (2015)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…