MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions-Reference-Cited by-同舟云学术

MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions

Published:2024 Issue:5 Volume:3 Page:908-918
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,Kirsch Andrea¹^ORCID,Lajer Joakim¹,Aalling-Frederiksen Olivia¹,Billinge Simon J. L.²^ORCID,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

Abstract

We present MLstructureMining, a machine learning tool that identifies a structural model from an experimental pair distribution function. We show how the method can be used for structure analysis of both crystalline and nanocrystalline materials.

Funder

H2020 European Research Council

Deutsche Forschungsgemeinschaft

Villum Fonden

Danmarks Grundforskningsfond

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00001C

Reference52 articles.

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

2. The Problem with Determining Atomic Structure at the Nanoscale

3. Size-induced amorphous structure in tungsten oxide nanoparticles

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

5. Structure analysis of supported disordered molybdenum oxides using pair distribution function analysis and automated cluster modelling