ERC-Projekt MuDiLingo

The European Research Council (ERC) awarded Prof. Sandfeld for his project proposal "A Multiscale Logo ERCDislocation Language for Data-Driven Materials Science" (MuDiLingo) an ERC Starting Grant of approx. 1.5 Mio. Euro. ERC grants are among the most important and competitive European fundings means, that are awarded for research ideas of particular scientific excellence and novelty. The grant allows to perform research over the next 5 years at the forefront of the scientific field.

The Background: Crystalline defects in metals and semiconductors are responsible for a wide range of mechanical, optical and electronic properties. Controlling the evolution of dislocations, i.e. line-like defects and the carrier of plastic deformation, interacting both among themselves and with other microstructure elements allows tailoring material behaviors on the micro and nanoscale. This is essential for rational design approaches towards next generation materials with superior mechanical properties.

The Problem: For nearly a century, materials scientists have been seeking to understand how dislocation systems Picture for articleevolve. In-situ microscopy now reveals complex dislocation networks in great detail. However, without a sufficiently versatile and general methodology for extracting, assembling and compressing dislocation-related information the analysis of such data often stays at the level of “looking at images” to identify mechanisms or structures. Simulations are increasingly capable of predicting the evolution of dislocations in full detail. Yet, direct comparison, automated analysis or even data transfer between small scale plasticity experiments and simulations is impossible, and a large amount of data cannot be reused.

The Project Vision: The vision of MuDiLingo is to develop and establish for the first time a Unifying Multiscale Language of Dislocation Microstructures. Bearing analogy to audio data conversion into MP3, this description of dislocations uses statistical methods to determine data compression while preserving the relevant physics. It allows for a completely new type of high-throughput data mining and analysis, tailored to the specifics of dislocation systems. This revolutionary data-driven approach links models and experiments on different length scales thereby guaranteeing true interoperability of simulation and experiment. Furthermore, machine-learning-based microstructure models will allow to reach length and time-scales that, so far, are not accessible to classical simulation methods. The application to technologically relevant materials will answer fundamental scientific questions and guide towards design of superior structural and functional materials.

 

List of Publications

2019

  • V. Samaee, S. Sandfeld, H. Idrissi, J. Groten, T. Pardoen, R. Schwaiger, D. Schyvers: "Dislocation structures
    and the role of grain boundaries in cyclically deformed Ni micropillars"
    Materials Science and Engineering: A, 2019
    DOI: 10.1016/j.msea.2019.138295    BibTeX

  • D. Steinberger, H. Song, S. Sandfel: "Machine Learning-Based Classification of Dislocation Microstructures"
    Frontiers in Materials, 6, Article 141
    DOI: 10.3389/fmats.2019.0141   BibTeX

2018

 

  • A. Prakash, S. Sandfeld; "Chances and Challenges in fusing data science with materials science"
    Practical Metallography, 55(8), pp. 493-514
    DOI: 10.3139/147.110539   BibTeX

  • P. Felfer, S. Sandfeld; "Digitalisation in Materials Science - Combination Experiments and Simulation Across Length Scales"
    Practical Metallography, 55(8), pp. 492
    DOI: 10.3139/147.018081   BibTeX

  • I. Issa, M. Alfreider, D. Darjan, O. Kolednik, S. Sandfeld, D. Kiener; "Linking Macroscopic Fracture Properties to Single Dislocation Processes"
    Microscopy and Microanalysis, 24(S1), pp. 2184-2185
    DOI: 10.1017/S1431927618011406   BibTeX

  • R. Kositski, D. Steinberger, S. Sandfeld, D. Mordehai; "Shear relaxation behind the shock front in 〈1 1 0〉 molybdenum - From the atomic scale to continuous dislocation fields"
    Computational Materials Science, 149(2018), pp. 125-133
    DOI: 10.1016/j.commatsci.2018.02.058   BibTeX