Master thesis: Machine learning based analysis of powder X-ray diffraction patterns
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Faculty / Division:
Department of Physics
We are looking for physics students with experience in Python, fun in programming and ideally with first experiences with machine learning (ML) methods for exciting and ambitious Master’s thesis projects.
In the context of state-of-the-art automated high-throughput experiments, automatic data analysis methods are becoming essential, since manual data analysis quickly becomes infeasible. The goal of this project is the automated analysis of powder X-ray diffraction patterns (pXRD) with neural networks. pXRD is a method for the structural resolution of crystals which is used in a large number of physics and materials science laboratories worldwide.
The work offers the possibility to develop state-of-the-art ML models such as ResNet on large synthetic data sets and to apply them to the automated analysis of experimental data. Furthermore, you will learn to work with high performance computing resources.
Based on prior work by the AiMat laboratory on pXRD space group classification, you will be able to use and further develop an automated computational workflow for the simulation of diffractograms of synthetically generated crystal structures, which are used as training data for supervised learning of deep CNN models (e.g. ResNet).
Currently, there are several possibilities and ideas to go beyond state-of-the-art methods developed in our group and reported in literature. Details can be discussed individually adapted to your interests. Possible directions (but not all of them have to be worked on!):
- ML Model development: Further development of ML models to predict lattice parameters and unit cell structure.
- Transfer from synthetic to experimental data: Collecting and collating of experimental data for transfer and testing. Synthetic diffractograms must be adapted to match experimental data.
- Multi-phase XRD: Distinguishing single-phase from multi-phase based on the symmetry elements. Development of appropriate synthetic data and ML models.