Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification...
Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction
About this item
Full title
Author / Creator
Publisher
Weinheim: John Wiley & Sons, Inc
Journal title
Language
English
Formats
Publication information
Publisher
Weinheim: John Wiley & Sons, Inc
Subjects
More information
Scope and Contents
Contents
Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational au...
Alternative Titles
Full title
Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_760e235e86aa4ae08c5a79c88276d052
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_760e235e86aa4ae08c5a79c88276d052
Other Identifiers
ISSN
2640-4567
E-ISSN
2640-4567
DOI
10.1002/aisy.202200042