Hard Materials Seminar - "Breaking Mechanical Property Trade-Offs in Metals: From Fundamental Mechanism Discovery to Spatial Intelligence–Driven Research"

Mechanical property trade-offs, such as strength–ductility and fatigue resistance–strength, are often regarded as intrinsic limits in structural metals. However, these trade-offs emerge from the spatial organization and characteristics of plasticity at the microstructural scale rather than from unavoidable material constraints.

This seminar presents transformative approaches to overcoming these trade-offs, spanning the traditional paradigm of physics-based experimental discovery to emerging strategies enabled by large-scale database generation and machine-learning–driven research.

From the ways of the past, we identified a deformation regime in which specific competing deformation mechanisms are activated, leading to a massive homogenization of plastic deformation. We established the mechanism of Dynamic Plastic Deformation Delocalization, demonstrating that controlled competition between deformation mechanisms can inhibit plastic localization and break the classical fatigue strength–performance trade-off.

In the new era of data, we developed a machine-learning framework termed Material Spatial Intelligence to systematically identify microstructural states capable of breaking mechanical property trade-offs.

Within this framework, we leverage automated database generation, high-resolution microstructure–plasticity latent mapping, and structured latent representations to encode and navigate the relationship between microstructure, deformation behavior, and mechanical properties.

This approach enables the data-driven identification of microstructural configurations that overcome classical performance limits and trade-offs, while providing fundamental understanding of microstructural effects and deformation processes.