ArticleDigital discovery2023
DeepStruc: towards structure solution from pair distribution function data using deep generative models.
Article in Digital discovery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Tracking Out-Of-Equilibrium Growth of Bimetallic Nanoalloys Using Deep Learning and X-ray Scattering.ACS nano · 2026Article
- Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models.Nature materials · 2025Article
- Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science · 2025Article
- Monitoring the Structural Changes in Iridium Nanoparticles during Oxygen Evolution Electrocatalysis withJournal of the American Chemical Society · 2024Article
- MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions.Digital discovery · 2024Article
- ClusterFinder: a fast tool to find cluster structures from pair distribution function data.Acta crystallographica. Section A, Foundations and advances · 2024Article
- Article
- Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.Chemical science · 2023Review
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Authors and funding
6 authors.
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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
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