# Atomic nonaffinity as a predictor of plasticity in amorphous solids

@article{Xu2021AtomicNA, title={Atomic nonaffinity as a predictor of plasticity in amorphous solids}, author={Bin Xu and Michael L. Falk and Sylvain Patinet and Pengfei Guan}, journal={Physical Review Materials}, year={2021} }

Structural heterogeneity of amorphous solids present difficult challenges that stymie the prediction of plastic events, which are intimately connected to their mechanical behavior. Based on a perturbation analysis of the potential energy landscape, we derive the atomic nonaffinity as an indicator with intrinsic orientation, which quantifies the contribution of an individual atom to the total nonaffine modulus of the system. We find that the atomic nonaffinity can efficiently characterize the…

## 7 Citations

Insights from the quantitative calibration of an elasto-plastic model from a Lennard-Jones atomic glass

- Materials Science
- 2021

We compare the macroscopic and the local plastic behavior of a model amorphous solid based on two radically different numerical descriptions. On the one hand, we simulate glass samples by atomistic…

Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

- Materials ScienceNature communications
- 2021

A rotationally non-invariant local structure representation is introduced that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations.

Predicting plasticity in disordered solids from structural indicators

- Materials SciencePhysical Review Materials
- 2020

Amorphous solids lack long-range order. Therefore identifying structural defects --- akin to dislocations in crystalline solids --- that carry plastic flow in these systems remains a daunting…

Evaluating the predictive power of machine learning model for shear transformation in metallic glasses using metrics for an imbalanced dataset

- Computer ScienceFrontiers in Materials
- 2022

This study trains a graph neural network that predicts the initially activated STZ and evaluates its predictive power using various metrics considered to be proper for handling imbalanced datasets, finding that the AUC value is significantly overestimated due to the class imbalance and too many atoms are misclassified as initial STZ.

Ergodic Structural Diversity Predicts Dynamics in Amorphous Materials

- PhysicsFrontiers in Materials
- 2022

Identification of flexible local environments from a disordered medium has been a long-standing challenge. Here, we introduce a time-relevant structural Shannon entropy as a unique feature of the…

Predicting plastic events and quantifying the local yield surface in 3D model glasses

- Materials ScienceJournal of the Mechanics and Physics of Solids
- 2021

Simple and Broadly Applicable Definition of Shear Transformation Zones.

- Materials SciencePhysical review letters
- 2021

The proposed definition of plasticity carriers in amorphous solids is more broadly applicable compared to anharmonic definitions put forward previously and is offered as an open-source library that analyzes low-lying STZs in computer glasses and in laboratory materials for which the harmonic approximation is accessible.

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