Identifying microscopic factors that influence ductility in disordered solids

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Mechanics

Background:

• Empirical strategies for controlling strain localization in disordered solids lack a unifying theoretical framework.
• Existing methods are system-specific, limiting broader applicability and understanding.
• A theoretical model is needed to explain the mechanisms behind strain localization tuning.

Purpose of the Study:

• To develop a theoretical framework for understanding and predicting strain localization in disordered solids.
• To construct structuro-elastoplastic (StEP) models applicable across different disordered systems.
• To elucidate the microscopic origins of varying ductility in materials.

Main Methods:

• Studied three model disordered solids: atomic glass, granular packing, and polymer glass.
• Employed machine learning to identify 'softness' as a key descriptor for local structural stability.
• Developed StEP models based on correlations between softness and structural rearrangements.

Main Results:

• StEP models achieved semiquantitative agreement with stress-strain curves and softness statistics across all studied systems.
• The models successfully predicted changes in ductility based on initial structure, rearrangement effects, and size.
• Identified key microscopic features governing strain localization without additional parameters.

Conclusions:

• StEP models provide a microscopic understanding of strain localization dependence on structure, plasticity, and elasticity.
• The developed models offer a generalizable theoretical approach to disordered material behavior.
• This work bridges the gap between empirical observations and theoretical explanations in materials science.