High-throughput computer model predicts diffusion data for the transport of light elements within so
High-throughput computer model predicts diffusion data for the transport of light elements within solids
The diffusion of light elements into metals has been efficiently modeled by A*STAR researchers using a machine learning approach.
Solid-state diffusion, in which atoms migrate through the lattice of a host material, underpins a variety of important processes that range from undesirable (corrosion) to useful (metal-joining processes). In one mechanism called ‘interstitial diffusion,“ light elements, such as nitrogen, move through lattices made up of much bigger atoms, such as metals, by squeezing between them. Yingzhi Zeng and colleagues at the A*STAR Institute of High Performance Computing have now developed a rapid predictive model for this phenomenon.
"Typical examples of interstitial diffusion include surface hardening of steel through carburization or nitridation, and the diffusion of oxygen in titanium for the design of implant and aerospace alloys,” Zeng says. This process is important to understand, but particularly difficult to probe experimentally. The challenge stems from the heavy-duty specialized equipment that is often required, and because as Zeng explains, “most experimental techniques rely on surface measurements, and so are inherently limited to a few nanometers under the surface.”
