Periodic Reporting for period 3 - MuDiLingo (A Multiscale Dislocation Language for Data-Driven Materials Science)
Période du rapport: 2021-01-01 au 2022-06-30
Plasticity is mainly carried by dislocations – line-like defects in the crystal lattice that are responsible for a large range of mechanical properties of crystalline solids. Controlling the evolution of dislocations interacting both among themselves and with the other microstructures allows tailoring material behaviors on the micro and nanoscale. This is essential for microscale devices and rational design approaches towards next generation materials with superior properties.
For nearly a century, materials scientists have been seeking to understand how dislocation systems evolve. In-situ microscopy now reveals complex dislocation networks in great detail. However, due to the lack of 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 watching “images” to identify mechanisms or structures. Simulations are increasingly capable of predicting the evolution of dislocations in full detail.
Yet, a 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 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 systems of dislocations. This revolutionary approach will link different models and experiments on different length scales thereby guaranteeing true interoperability of simulations and experiments. The application to technologically relevant materials will answer fundamental scientific questions and guide towards the design of superior materials in the spirit of ICME.
For nearly a century, materials scientists have been seeking to understand how dislocation systems evolve. In-situ microscopy now reveals complex dislocation networks in great detail. However, due to the lack of 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 watching “images” to identify mechanisms or structures. Simulations are increasingly capable of predicting the evolution of dislocations in full detail.
Yet, a 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 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 systems of dislocations. This revolutionary approach will link different models and experiments on different length scales thereby guaranteeing true interoperability of simulations and experiments. The application to technologically relevant materials will answer fundamental scientific questions and guide towards the design of superior materials in the spirit of ICME.
1. learning python/C++
2. development of an elastic eigenstrain solver
3. creating of initial dislocation structures from DDD and MD
4. coarse graining and analysis of dislocation structures
5. first steps towards "machine learning dislocations"
6. creation of “synthetic” TEM images for machine learning
7. learning how to apply Deep Learning to TEM images
8. First steps towards segmentation of generic TEM images of dislocations
9. Sample preparation, testing and in-situ microscopy
10. Many discussions with the experimentalists how to obtain TEM images that are suitable for Machine Learning
2. development of an elastic eigenstrain solver
3. creating of initial dislocation structures from DDD and MD
4. coarse graining and analysis of dislocation structures
5. first steps towards "machine learning dislocations"
6. creation of “synthetic” TEM images for machine learning
7. learning how to apply Deep Learning to TEM images
8. First steps towards segmentation of generic TEM images of dislocations
9. Sample preparation, testing and in-situ microscopy
10. Many discussions with the experimentalists how to obtain TEM images that are suitable for Machine Learning
We were the first to use machine learning to statistically analyse and characterize dislocation microstructures