ArticleNature communications2021
Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A structural disorder function linking local symmetry breaking to plastic indicators and strength in amorphous solids.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cooperative atomic motion during shear deformation in metallic glass.Nature communications · 2026Article
- Persistent homology elucidates hierarchical structures responsible for mechanical properties in covalent amorphous solids.Nature communications · 2025Article
- Heat Source Parameter Identification Based on Attention-Enhanced Residual Convolutional Neural Network.Materials (Basel, Switzerland) · 2025Article
- Hedgehog topological defects in 3D amorphous solids.Nature communications · 2025Article
- Unveiling hidden particle-level defects in glasses.Nature communications · 2025Article
- Predicting the pathways of string-like motions in metallic glasses via path-featurizing graph neural networks.Science advances · 2024Article
- Microscopic mechanisms of pressure-induced amorphous-amorphous transitions and crystallisation in silicon.Nature communications · 2024Article
- SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network.International journal of molecular sciences · 2024Article
- Identifying microscopic factors that influence ductility in disordered solids.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Topology of vibrational modes predicts plastic events in glasses.Nature communications · 2023Article
- Shear transformations in metallic glasses without excessive and predefinable defects.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Predicting the failure of two-dimensional silica glasses.Nature communications · 2022Article
- Tension-compression asymmetry in amorphous silicon.Nature materials · 2021Article
- Inverse design of glass structure with deep graph neural networks.Nature communications · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural network (CNN), a powerful deep learning algorithm, leads to unprecedented accuracy for identifying atoms with high propensity for shear transformations (i.e., plastic susceptibility), solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses.
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