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

Area of Science:

• Materials Science
• Nanotechnology
• Computational Chemistry

Background:

• Real-time reconstruction of metallic nanoalloy growth is challenging due to complex atomic arrangements.
• Distinct structures and properties arise from nanoalloys compared to pure metals.
• Existing methods struggle with in-situ analysis of dynamic nanoalloy formation.

Purpose of the Study:

• To develop a methodology for direct, data-driven structural interpretation of nanoalloy growth using X-ray scattering.
• To enable real-time extraction of atomic-scale descriptors (size, composition, ordering) from experimental data.
• To reconstruct and understand the growth mechanism of bimetallic nanoparticles under non-equilibrium conditions.

Main Methods:

• Integration of Molecular Dynamics simulations with in silico wide-angle X-ray scattering (WAXS) signals.
• Calculation of WAXS signals using the Debye Scattering Equation.
• Application of a deep learning regressor to connect experimental WAXS patterns with atomic-scale structural information.

Main Results:

• A novel framework enabling direct extraction of nanoparticle size, composition, and elemental ordering (Janus-like to core-shell) from WAXS patterns.
• Successful reconstruction of the in situ growth mechanism of Silver-Cobalt (Ag-Co) nanoparticles.
• Evidence that Ag-Co nanoparticle evolution is dictated by a kinetically controlled pathway, not solely by material supply.

Conclusions:

• The developed deep learning framework significantly enhances the real-time interpretation of nanoalloy growth via X-ray scattering.
• This approach overcomes limitations of conventional fitting methods for complex nanoalloy systems.
• The methodology is compatible with on-the-fly and high-throughput data collection, advancing nanoalloy research.