DeepStruc: towards structure solution from pair distribution function data using deep generative models

• Published in: Digital discovery 2023-02, Vol.2 (1), p.69-8
• Main Authors: Kjær, Emil T. S, Anker, Andy S, Weng, Marcus N, Billinge, Simon J. L, Selvan, Raghavendra, Jensen, Kirsten M. Ø
• Format: Article
• Language: English
• Subjects: Chemistry
• ISSN: 2635-098X; 2635-098X
• DOI: 10.1039/d2dd00086e

Structure solution of nanostructured materials that have limited long-range order remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple monometallic nanoparticle structure directly from a Pair Distribution Function (PDF) obtained from total scattering data by using a conditional variational autoencoder. We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution. We also apply DeepStruc to a system of hcp , fcc and stacking faulted nanoparticles, where DeepStruc recognizes stacking faulted nanoparticles as an interpolation between hcp and fcc nanoparticles and is able to solve stacking faulted structures from PDFs. Our findings suggests that DeepStruc is a step towards a general approach for structure solution of nanomaterials. We present a deep learning algorithm, DeepStruc, that can solve a simple nanoparticle structure directly from an experimental Pair Distribution Function (PDF) by using a conditional variational autoencoder.