Speaker
Description
Traditionally, the analysis of Laue diffraction pattern, crucial for determining the crystal orientation, has been a time-consuming process, requiring manual input of a skilled user. The development of an fully autonomous recognition tools aims to streamline this procedure, enhance accuracy, and to enable automation of various tasks such as crystal coalignment [1].
Existing Laue orienting software (for example OrientExpress, QLaue [2], LauePt [3] or LaueTools [4]) requires manual input and cannot solve Laue patterns automatically. In the recent years, problem is approached via machine learning. A paper by Purohit, et al. (LaueNN [5]) exlores a use of a preceptron architecture. Another possibility is the use of reinforced learning. On the other hand, images form x-ray detector itself could be directly processed using convolutional networks or generative models. The spatial correlation of data, the reflection spots, suggests a potential use of graph convolutional networks. We will discuss all these approaches and show our proposals for general problem of automatic Laue pattern solving.
[1] see contribnution "ALSA: Automatic Laue Sample Aligner", https://mambaproject.cz/alsa
[2] https://github.com/stuwilkins/QLaue
[3] https://github.com/yaafeiliu/LauePt4
[4] https://gitlab.esrf.fr/micha/lauetools/
[5] Purushottam Raj Purohit, Ravi Raj Purohit, et al., Journal of Applied Crystallography 55.4 (2022).
