# Predicting elastic and plastic properties of small iron polycrystals by machine learning

## TL;DR

This study combines molecular dynamics and machine learning to predict the elastic and plastic properties of small iron polycrystals, revealing better predictability for shear modulus than yield stress.

## Contribution

It introduces a convolutional neural network approach to predict mechanical properties from initial microstructure, highlighting differences in predictability for shear modulus and yield stress.

## Key findings

- Shear modulus prediction is more accurate than yield stress.
- Initial microstructure strongly influences mechanical response.
- Machine learning can capture complex structure-property relationships.

## Abstract

Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the sample-dependent shear moduli and yield stresses of a large set of small cube-shaped iron polycrystals generated by Voronoi tesselation, by combining molecular dynamics simulations and machine learning. Training a convolutional neural network to infer the mapping between the initial polycrystalline structure of the samples and features of the ensuing stress-strain curves reveals that the shear modulus can be predicted better than the yield stress. We discuss our results in the context of the sensitivity of the system's response to small perturbations of its initial state.