Tracking Out-Of-Equilibrium Growth of Bimetallic Nanoalloys Using Deep Learning and X-ray Scattering

High Resolution Image Download MS PowerPoint Slide Reconstructing the growth of metallic nanoparticles in real time remains a long-standing challenge, particularly for nanoalloy systems that exhibit complex atomic spatial arrangements and display structures and properties distinct from those of pure metal particles. Here, we present a methodology that integrates Molecular Dynamics and in silico wide-angle X-ray scattering signals, which are calculated using the Debye Scattering Equation, with a deep learning regressor, enabling a direct connection between experiments and data-driven structural interpretation. Atomic-scale descriptors of size and composition of bimetallic nanoparticles, as well as elemental ordering spanning from Janus-like to core–shell arrangements, previously accessible primarily through computational methods, are now extracted using X-ray scattering measured patterns as the sole input information. Applying this model, we reconstruct the growth mechanism of Ag–Co nanoparticles formed in ultra high vacuum by Co vapor deposition onto Ag seeds, monitored in situ and in real time by grazing incidence wide-angle X-ray scattering and occurring under strongly out-of-equilibrium conditions. This analysis provides evidence that the nanoparticle structural evolution is not governed solely by the amount of Co supplied but instead results from a kinetically controlled evolution pathway, in line with theoretical predictions. Overall, the developed framework provides a strategy that significantly improves the interpretation of nanoalloys growth processes in real time via X-ray scattering data that would otherwise be difficult or even impossible to analyze through conventional fitting approaches. As such, the presented method is inherently compatible with on-the-fly and high-throughput data collection strategies.