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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 <Title>Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems</Title>

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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