Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session X05: Machine Learning in Nonlinear Physics and Mechanics I
8:00 AM–10:36 AM,
Friday, March 19, 2021
Room: 05
Sponsoring
Units:
DSOFT GSNP DCOMP
Chair: Christopher Rycroft, Harvard University; Shmuel Rubinstein, Harvard University
Abstract: X05.00008 : Large-scale visualization with machine learning of dislocation networks in colloidal single crystals
9:48 AM–10:00 AM
Live
Presenter:
Ilya Svetlizky
(Harvard University)
Authors:
Ilya Svetlizky
(Harvard University)
Seongsoo Kim
(Harvard University)
Seong Ho Pahng
(Harvard University)
Agnese Curatolo
(Harvard University)
Michael Brenner
(Harvard University)
David Weitz
(Harvard University)
Frans A Spaepen
(Harvard University)
To visualize the large-scale collective dislocation dynamics in colloidal single crystals, we developed a laser diffraction imaging technique inspired by the classical TEM imaging methods in atomic systems. We harness convolutional neural networks to address the inverse problem of inferring the structure of the dislocation network from the multiple complex diffraction images. The training process, however, imposes a significant problem as experimental data is sparse. This difficulty is bypassed by generating a training dataset composed of multiple simulated random dislocation structures. We will show that the trained neural network reliably reconstructs the spatial positions of the dislocations and their Burgers vectors by testing the results with in-situ confocal microscopy data on a smaller portion of the sample. We demonstrate the success of our method by applying it to the network of misfit dislocations formed during particle sedimentation on a strained substrate.
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