Geometrical fingerprints of shear transformation zones in network glasses
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916659167494144 |
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| author | Bamer, Franz Wu, Zhao Alshabab, Somar Shekh |
| author_facet | Bamer, Franz Wu, Zhao Alshabab, Somar Shekh |
| contents | Oxide glasses have the structure of disordered covalent networks that are accountable for their mechanical response. Identifying the topological phenomena of the elastic structural response, we statistically backpropagate local regions that have the highest susceptibility of rearrangement. Shear transformation zones in network glasses highly correlate with regions of the highest variance in their bond stretch distributions projected into the direction of macroscopic deformation. However, directional influence is significantly less essential than bond stretch variance, which shows that shear transformation zones in network glasses are mainly state-dependent. Exclusively depending on the local geometry of the initial material state, our indicators are physically informing and can be evaluated directly from images with insignificant computational effort. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_17210 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geometrical fingerprints of shear transformation zones in network glasses Bamer, Franz Wu, Zhao Alshabab, Somar Shekh Disordered Systems and Neural Networks Materials Science Statistical Mechanics Oxide glasses have the structure of disordered covalent networks that are accountable for their mechanical response. Identifying the topological phenomena of the elastic structural response, we statistically backpropagate local regions that have the highest susceptibility of rearrangement. Shear transformation zones in network glasses highly correlate with regions of the highest variance in their bond stretch distributions projected into the direction of macroscopic deformation. However, directional influence is significantly less essential than bond stretch variance, which shows that shear transformation zones in network glasses are mainly state-dependent. Exclusively depending on the local geometry of the initial material state, our indicators are physically informing and can be evaluated directly from images with insignificant computational effort. |
| title | Geometrical fingerprints of shear transformation zones in network glasses |
| topic | Disordered Systems and Neural Networks Materials Science Statistical Mechanics |
| url | https://arxiv.org/abs/2503.17210 |