DeepStruc: towards structure solution from pair distribution function data using deep generative models-Reference-Cited by-同舟云学术

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

Published:2023 Issue:1 Volume:2 Page:69-80
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Kjær Emil T. S.¹^ORCID,Anker Andy S.¹^ORCID,Weng Marcus N.¹,Billinge Simon J. L.²³^ORCID,Selvan Raghavendra⁴⁵,Jensen Kirsten M. Ø.¹^ORCID

Affiliation:

1. Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark

2. Department of Applied Physics and Applied Mathematics Science, Columbia University, New York, NY 10027, USA

3. Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA

4. Department of Computer Science, University of Copenhagen, 2100 Copenhagen Ø, Denmark

5. Department of Neuroscience, University of Copenhagen, 2200, Copenhagen N, Denmark

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.

Funder

H2020 European Research Council

Danmarks Grundforskningsfond

National Science Foundation

Villum Fonden

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00086E

Reference48 articles.

1. Structure determination from powder diffraction data

2. Crystallography with powders

3. Beyond crystallography: the study of disorder, nanocrystallinity and crystallographically challenged materials with pair distribution functions

4. Applications of pair distribution function methods to contemporary problems in materials chemistry

5. There's no place like real-space: elucidating size-dependent atomic structure of nanomaterials using pair distribution function analysis

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