Pathway to lowest-energy structures and stress relaxation for the surface triple junction verified by machine learning

TL;DR

This study uncovers the lowest-energy structures and stress relaxation mechanisms of surface triple junctions using experimental mapping, analysis, and machine learning verification, advancing understanding of microstructure evolution.

Contribution

It identifies the universal zipped Y-shaped notch as the lowest-energy structure of surface triple junctions and verifies this with machine learning across various boundaries.

Findings

Zipped Y-shaped notch is the universal lowest-energy structure.

Local stress mechanisms explain energetic preferences.

Machine learning confirms findings across boundary types.

Abstract

The behavior of surface triple junctions (STJ) at emergent grain boundaries on free surfaces is critical to the microstructure evolution, and therefore to the stability of the next generation interconnect. Yet,despite this significant importance, its lowest-energy structure and local stress have remained persistently unknown. Here, we fill this critical gap through high-resolution experimental mapping of the local surface deformation at STJ, the analysis of the local structure and stress relaxation, and ergodic searching metastable structures. We establish the zipped Y-shaped notch as the universal lowest-energy structures. This energetic preference was well explained by the distinctive local stress mechanism and was excellently verified with machine learning methods for a wide range of boundaries. By revealing the elusive thermodynamics of STJs, our findings advance the research field by redefining the energetic framework for capillary driven structure evolution and providing foundation for understanding kinetically diffusive deformation and for engineering thin-film interconnects and related materials.