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1 Ergebnisse
1
Machine Learning-Assisted Selection of Active Spaces for St..:
Pavlo Golub (4431865)
;
Andrej Antalik (11485893)
;
Libor Veis (2347693)
.
https://figshare.com/articles/journal_contribution/Machine_Learning-Assisted_Selection_of_Active_Spaces_for_Strongly_Correlated_Transition_Metal_Systems/16687653. , 2021
Link:
https://doi.org/10.1021/acs.jctc.1c00235.s001
RT Journal T1
Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems
UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16687653&Exemplar=1&LAN=DE A1 Pavlo Golub (4431865) A1 Andrej Antalik (11485893) A1 Libor Veis (2347693) A1 Jiri Brabec (1487755) YR 2021 K1 Biochemistry K1 Science Policy K1 Astronomical and Space Sciences not elsewhere classified 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 small benchmark molecules K1 one transition metal K1 transition metal systems K1 artificial systems composed K1 including bimetallic complexes K1 active space orbitals K1 active space K1 active spaces K1 model systems K1 tremendous value K1 training set K1 spin states K1 site entropy K1 selected set K1 recent studies K1 reasonable accuracy K1 proper choice K1 nontrivial task K1 neural network K1 natural sciences K1 machine learning K1 lowest states K1 energy difference K1 dmrg level K1 based approach K1 automatic selection K1 assisted selection K1 accurate description JF https://figshare.com/articles/journal_contribution/Machine_Learning-Assisted_Selection_of_Active_Spaces_for_Strongly_Correlated_Transition_Metal_Systems/16687653 LK http://dx.doi.org/https://doi.org/10.1021/acs.jctc.1c00235.s001 DO https://doi.org/10.1021/acs.jctc.1c00235.s001 SF ELIB - SuUB Bremen
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