Machine learning-augmented modeling on the formation of Si-dominated Non-β″ early-stage precipitates in Al-Si-Mg alloys with Si supersaturation induced by non-equilibrium solidification

Recent experiments have shown that Al-Si-Mg alloys solidified under high cooling rates may lead to the nucleation of Si-enriched clusters that are remarkably different from the conventional Mg-Si co-clusters (e.g. β″ particles), and yet the responsible mechanism remains to be elucidated. Here we tackle the problem using a multiscale modeling framework that integrates atomistic modeling, energy landscape sampling, and lattice-based kinetic Monte Carlo (kMC) simulation. The migration energy barriers for vacancy-mediated diffusion amid complex local chemical environments are predicted on-the-fly using a surrogate machine learning model. We discover that the actual vacancy-Si migration barriers are much lower than those assumed in the classic linear interpolation approximation. Such a strong deviation from conventional wisdom, in conjunction with differing Si solute composition, can lead to a great variety in the nucleated early-stage precipitates. More specifically, a high-level supersaturation of Si solute (i.e. $x_{Si}/\left(x_{Si}+x_{Mg}\right)>0.75$) would lead to an unexpectedly high enrichment of Si in the nucleated clusters with the Si:Mg ratio up to 5∼6; while at a lower-level supply of Si solute the Mg-Si co-clusters (i.e. Si:Mg ratio around 1∼2) are nucleated instead. These findings provide a viable explanation for the diverse types of early-stage precipitates observed in various experiments, from Si-enriched precipitates in high-pressure die cast Al alloys to β″ particles in conventional casting and/or heat-treated alloys. The implications of our findings are also discussed.