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

Emil Thyge Skaaning Kjær, Andy Sode Anker, Marcus Nørgaard Weng, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen

Keywords: X-ray scattering, Pair Distribution Function, Nanoparticle characterization, Materials Chemistry, Conditional variational autoencoder

TL;DR: We present a conditional variational autoencoder, DeepStruc, that can solve the structure of monometallic directly from Pair Distribution Function (PDF) data obtained from total scattering data.

Abstract: 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 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.

Paper Track: Papers

Submission Category: Automated Material Characterization