# Effects of Rate, Size and Prior Deformation in Microcrystal Plasticity

## TL;DR

This paper introduces a minimal dislocation model for microcrystal plasticity that explains effects of rate, size, and prior deformation, supported by experimental data and machine learning insights.

## Contribution

The study presents a benchmarked minimal model linking dislocation dynamics to size, rate, and deformation history effects in microcrystals, integrating machine learning analysis.

## Key findings

- Model reproduces experimental size and rate effects.
- Dislocation ensemble statistics reveal stress and displacement behaviors.
- Machine learning enhances understanding of microcrystal deformation.

## Abstract

Crystal plasticity of sub-micron finite volumes is characterized by the flow of emergent dislocation defects, giving rise to size effects in mechanical properties and avalanche phenomena. In this chapter, we present a minimal model for discrete edge dislocations in a finite volume, that has been benchmarked against experimental data and displays known phenomenological trends. We discuss how this model can explain seemingly disconnected effects of rate, size and prior deformation on microcrystal plasticity. We demonstrate the statistical features of dislocation ensembles for both stress and displacement controlled loading conditions and explore in detail the connection between loading rate and displacement bursts. Finally, we present model studies of machine learning algorithms in microcrystal plasticity that both improve understanding and clarify the range of such methods' usefulness. In this way, we elucidate the role of prior deformation history on micro and nano-sized specimens and we use this understanding to predict the mechanical response of thin films through microstructural observations of pre-existing dislocation configurations.