DeepStruc: towards structure solution from pair distribution function data using deep generative models. (6th December 2022)

• Record Type: Journal Article
• Authors: Kjær, Emil T. S.; Anker, Andy S.; Weng, Marcus N.; Billinge, Simon J. L.; Selvan, Raghavendra; Jensen, Kirsten M. Ø.
• Is Part Of: Digital discovery. Volume 2:Number 1(2023)
• Page Start: 69
• Page End: 80
• Publication Date: 2022-12-06
• Subjects: Chemistry -- Data processing -- Periodicals; Medical sciences -- Data processing -- Periodicals; Machine learning -- Periodicals; 542.85
• DOI: 10.1039/d2dd00086e
• Languages: English
• ISSNs: 2635-098X

Abstract

Abstract : 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. 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 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.