Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

Abstract

Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to the paucity of experimental techniques that can provide atomic-level information for catalytic metal species. Here we report on the use of X-ray absorption near-edge structure (XANES) spectroscopy and supervised machine learning (SML) for refining the 3D geometry of metal catalysts. SML is used to unravel the hidden relationship between the XANES features and catalyst geometry. To train our SML method, we rely on ab initio XANES simulations. Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated here by reconstructing the average size, shape, and morphology of well-defined platinum nanoparticles. This method is applicable to the determination of the nanoparticle structure in operando studies and can be generalized to other nanoscale systems. It also allows on-the-fly XANES analysis and is a promising approach for high-throughput and time-dependent studies.