Predicting the failure of two-dimensional silica glasses

Font-Clos F, Zanchi M, Hiemer S, Bonfanti S, Guerra R, Zaiser M, Zapperi S (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 13

Journal Issue: 1

DOI: 10.1038/s41467-022-30530-1

Abstract

The sheer number of parameters in deep learning makes the physical interpretation of failure predictions in glasses challenging. Here the authors use Grad-CAM to reveal the role of topological defects and local potential energies in failure predictions.

Authors with CRIS profile

Stefan Hiemer Lehrstuhl für Werkstoffsimulation Michael Zaiser Lehrstuhl für Werkstoffsimulation

Additional Organisation(s)

Graduiertenkolleg 2423 Skalenübergreifende Bruchvorgänge: Integration von Mechanik, Materialwissenschaften, Mathematik, Chemie und Physik

Involved external institutions

Università degli studi di Milano IT Italy (IT)

How to cite

APA:

Font-Clos, F., Zanchi, M., Hiemer, S., Bonfanti, S., Guerra, R., Zaiser, M., & Zapperi, S. (2022). Predicting the failure of two-dimensional silica glasses. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-30530-1

MLA:

Font-Clos, Francesc, et al. "Predicting the failure of two-dimensional silica glasses." Nature Communications 13.1 (2022).

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