New frontiers in the structural analysis of nanomaterials: a deep-learning leap to bridge the gap in crystalline order.

Abstract

High-resolution crystal structure determination using X-ray, electron or neutron diffraction is often limited by poor crystallinity, small crystallite size and instrumental factors, which cause significant diffraction peak broadening and a consequent loss of information. This challenge is particularly severe for nanocrystalline materials, whose X-ray powder diffraction (XRPD) profiles exhibit extensive peak overlap, thereby hindering the reliable extraction of structural information through conventional crystallographic methods. We present here a novel deep-learning-based approach to increase the probability of determining the structure of nanocrystals. A deep-learning model is trained to transform simulated diffraction patterns, degraded by reduced crystalline order, into optimal profiles representative of ideal perfectly crystalline materials. Instead of attempting direct structure inference, this approach improves data by making them better suited for standard crystallographic analyses. The resulting pipeline is both robust and versatile, demonstrating consistent performance across varying crystallite sizes on simulated profiles. The procedure has also been successfully applied to experimental data. To our knowledge, this is the first application of deep learning to enhance experimental XRPD data, representing a breakthrough in the structural characterization of nanocrystalline materials, specifically by enabling progress in difficult cases where atomic-level structure determination is hindered by poor diffraction data.