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1 Ergebnisse
1
Machine Learning Prediction of the Experimental Transition ..:
Vyshnavi Vennelakanti
;
Irem B. Kilic
;
Gianmarco G. Terrones
..
doi:10.1021/acs.jpca.3c07104.s001. , 2023
Link:
https://doi.org/10.1021/acs.jpca.3c07104.s001
RT Journal T1
Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes
UL https://suche.suub.uni-bremen.de/peid=base-ftunivoxfordfig:oai:figshare.com:article_24906112&Exemplar=1&LAN=DE A1 Vyshnavi Vennelakanti A1 Irem B. Kilic A1 Gianmarco G. Terrones A1 Chenru Duan A1 Heather J. Kulik YR 2023 K1 Biochemistry K1 Genetics K1 Cancer K1 Biological Sciences not elsewhere classified K1 Mathematical Sciences not elsewhere classified K1 Chemical Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 vennelakanti et al K1 upon excluding outliers K1 machine learning prediction K1 larger data sets K1 enthalpic contributions needed K1 based revised autocorrelations K1 024120 (< b K1 transition temperature close K1 transition temperature (< K1 achieving moderate correlation K1 experimental transition temperature K1 expect ml models K1 ml models predict K1 ml models considered K1 2 </ sub K1 18 sco complexes K1 previously curated sco K1 95 data set K1 crossover complexes spin K1 room temperature K1 ml models K1 sco complexes K1 >< sub K1 compare ml K1 strong correlation K1 spin state K1 r </ K1 potential applications K1 phys </ K1 pearson ' K1 molecular electronics K1 model transferability K1 model training K1 large errors K1 external stimuli K1 exhibit changes K1 design ligands K1 delicate balance K1 159 ,</ JF doi:10.1021/acs.jpca.3c07104.s001 LK http://dx.doi.org/https://doi.org/10.1021/acs.jpca.3c07104.s001 DO https://doi.org/10.1021/acs.jpca.3c07104.s001 SF ELIB - SuUB Bremen
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