TY  - JOUR
AU  - Song, Hengxu
AU  - Nguyen, Binh Duong
AU  - Govind, Kishan
AU  - Berta, Dénes
AU  - Ispánovity, Péter Dusán
AU  - Legros, Marc
AU  - Sandfeld, Stefan
TI  - Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations
JO  - Acta materialia
VL  - 282
SN  - 1359-6454
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2024-06168
SP  - 120455 -
PY  - 2025
AB  - In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe “stick–slip motion” of single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001349894500001
DO  - DOI:10.1016/j.actamat.2024.120455
UR  - https://juser.fz-juelich.de/record/1032346
ER  -