Predicting the failure of two-dimensional silica glasses (Q6708135)

Datasets for the paper: Font-Clos F, Zanchi M, Hiemer S, Bonfanti S, Guerra R, Zaiser M, Zapperi S. Predicting the failure of two-dimensional silica glasses. arXiv preprint arXiv:2201.09723. 2022 Jan 24. https://arxiv.org/abs/2201.09723 The datasets can be used together with the codes available at https://github.com/ComplexityBiosystems/2D-silica-ML to reproduce the resuts of the paper above. In particular, the datasets contain the the following data The dataset 20201222-var02-onethousand.zip contains configurations at fixed disorder (s2= 0.2). The dataset 20210209-variable-disorder.zip contains configurations at variable disorder. The dataset ml_dataset_fracture_atoms_var02.tar.gz contains the images for the prediction of the crack path. The dataset ml_dataset_var02.tar.gz contains images of silica at fixed disorder (s2= 0.2). for the prediction of disorder, rupture strain and location. The dataset ml_dataset_variable_var.tar.gz contains images of silica at variable disorderfor the prediction of disorder, rupture strain and location. The dataset trained_models contains an example of a ResNet trained for predicting rupture strain at variable disorder (it can be used for testing the Grad-CAM method).