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1 Ergebnisse
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Deep Learning Models to Identify Common Phases across Mater..:
Nam Q. Le
;
Michael Pekala
;
Alexander New
...
doi:10.1021/acs.jpcc.3c05147.s001. , 1753
Link:
https://doi.org/10.1021/acs.jpcc.3c05147.s001
RT Journal T1
Deep Learning Models to Identify Common Phases across Material Systems from X‑ray Diffraction
UL https://suche.suub.uni-bremen.de/peid=base-ftdeakinunifig:oai:figshare.com:article_24463341&Exemplar=1&LAN=DE A1 Nam Q. Le A1 Michael Pekala A1 Alexander New A1 Edwin B. Gienger A1 Christine Chung A1 Timothy J. Montalbano A1 Elizabeth A. Pogue A1 Janna Domenico A1 Christopher D. Stiles YR 1753 K1 Medicine K1 Biological Sciences not elsewhere classified K1 Chemical Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 two different sources K1 simple yet effective K1 materials project ) K1 materials discovery settings K1 deep learning models K1 like phases based K1 unexplored material systems K1 many a15 phases K1 improving performance across K1 type phases based K1 materials discovery K1 material systems K1 tested across K1 relevant phases K1 particular phases K1 expected phases K1 distinguishable based K1 based patterns K1 based data K1 systems spanning K1 test sets K1 synthetic data K1 strongly tied K1 simulated noise K1 ray diffraction K1 novel superconductors K1 new compounds K1 metallic alloys K1 indispensable tool K1 include examples K1 ii superconductors K1 high proportion K1 good performance K1 extend readily K1 experimental data K1 equally high K1 classify a15 K1 case study K1 careful consideration K1 broad space K1 automated workflows K1 augmenting dft K1 approach demonstrated K1 also observed K1 allowing researchers K1 23 elements JF doi:10.1021/acs.jpcc.3c05147.s001 LK http://dx.doi.org/https://doi.org/10.1021/acs.jpcc.3c05147.s001 DO https://doi.org/10.1021/acs.jpcc.3c05147.s001 SF ELIB - SuUB Bremen
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