Work

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

Year: 2022

Type: article

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.

Source: Digital Discovery

DOI: 10.1039/d2dd00086e

Authors: Emil T. S. Kjær, Andy S. Anker, Marcus N. Weng, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen

Institutions: University of Copenhagen, Brookhaven National Laboratory, Columbia University

Language: English

Topic: X-ray Diffraction in Crystallography

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Villum Fonden, European Commission, National Research Foundation, Danmarks Grundforskningsfond, European Research Council, H2020 European Research Council

Awards: G1854271015, G1939325950, G434337789, G4547360091, G4808946783, DMREF: Collaborative Research: Complex Nanofeatures in Crystals: Theory and Experiment Meet in the Cloud, Live Tapings of Material Formation: Unravelling formation mechanisms in materials chemistry through Multimodal X-ray total scattering studies, Center for High Entropy Alloy Catalysis (CHEAC), G8938256800

OpenAlex ID: W4311083417

FWCI: 1.85

Cites: 52

Cited by: 30

Related to: 10