scholarly journals Quantum Embedding Theory for Strongly Correlated States in Materials

Author(s):  
He Ma ◽  
Nan Sheng ◽  
Marco Govoni ◽  
Giulia Galli
2019 ◽  
Author(s):  
Riddhish Pandharkar ◽  
Matthew R. Hermes ◽  
Christopher J. Cramer ◽  
Laura Gagliardi

<p>Quantitatively accurate calculations for spin state ordering in transition-metal complexes typically demand a robust multiconfigurational treatment. The poor scaling of such methods with increasing size makes them impractical for large, strongly correlated systems. Density matrix embedding theory (DMET) is a fragmentation approach that can be used to specifically address this challenge. The single-determinantal bath framework of DMET is applicable in many situations, but it has been shown to perform poorly for molecules characterized by strong correlation when a multiconfigurational self-consistent field solver is used. To ameliorate this problem, the localized active space self-consistent field (LASSCF) method was recently described. In this work, LASSCF is applied to predict spin state energetics in mono- and di-iron systems and we show that the model offers an accuracy equivalent to CASSCF but at a substantially lower computational cost. Performance as a function of basis set and active space is also examined.<br></p>


2017 ◽  
Vol 95 (19) ◽  
Author(s):  
Klaas Gunst ◽  
Sebastian Wouters ◽  
Stijn De Baerdemacker ◽  
Dimitri Van Neck

2020 ◽  
Vol 22 (44) ◽  
pp. 25522-25527
Author(s):  
He Ma ◽  
Nan Sheng ◽  
Marco Govoni ◽  
Giulia Galli

Using a recently developed quantum embedding theory, we present first principles calculations of strongly correlated states of spin defects in diamond.


2019 ◽  
Author(s):  
Riddhish Pandharkar ◽  
Matthew R. Hermes ◽  
Christopher J. Cramer ◽  
Laura Gagliardi

<p>Quantitatively accurate calculations for spin state ordering in transition-metal complexes typically demand a robust multiconfigurational treatment. The poor scaling of such methods with increasing size makes them impractical for large, strongly correlated systems. Density matrix embedding theory (DMET) is a fragmentation approach that can be used to specifically address this challenge. The single-determinantal bath framework of DMET is applicable in many situations, but it has been shown to perform poorly for molecules characterized by strong correlation when a multiconfigurational self-consistent field solver is used. To ameliorate this problem, the localized active space self-consistent field (LASSCF) method was recently described. In this work, LASSCF is applied to predict spin state energetics in mono- and di-iron systems and we show that the model offers an accuracy equivalent to CASSCF but at a substantially lower computational cost. Performance as a function of basis set and active space is also examined.<br></p>


1989 ◽  
Vol 54 (1) ◽  
pp. 101-105 ◽  
Author(s):  
J. Bruce Tomblin ◽  
Cynthia M. Shonrock ◽  
James C. Hardy

The extent to which the Minnesota Child Development Inventory (MCDI), could be used to estimate levels of language development in 2-year-old children was examined. Fifty-seven children between 23 and 28 months were given the Sequenced Inventory of Communication Development (SICD), and at the same time a parent completed the MCDI. In addition the mean length of utterance (MLU) was obtained for each child from a spontaneous speech sample. The MCDI Expressive Language scale was found to be a strong predictor of both the SICD Expressive scale and MLU. The MCDI Comprehension-Conceptual scale, presumably a receptive language measure, was moderately correlated with the SICD Receptive scale; however, it was also strongly correlated with the expressive measures. These results demonstrated that the Expressive Language scale of the MCDI was a valid predictor of expressive language for 2-year-old children. The MCDI Comprehension-Conceptual scale appeared to assess both receptive and expressive language, thus complicating its interpretation.


2019 ◽  
Author(s):  
Sam G. B. Roberts ◽  
Anna Roberts

Group size in primates is strongly correlated with brain size, but exactly what makes larger groups more ‘socially complex’ than smaller groups is still poorly understood. Chimpanzees (Pan troglodytes) and gorillas (Gorilla gorilla) are among our closest living relatives and are excellent model species to investigate patterns of sociality and social complexity in primates, and to inform models of human social evolution. The aim of this paper is to propose new research frameworks, particularly the use of social network analysis, to examine how social structure differs in small, medium and large groups of chimpanzees and gorillas, to explore what makes larger groups more socially complex than smaller groups. Given a fission-fusion system is likely to have characterised hominins, a comparison of the social complexity involved in fission-fusion and more stable social systems is likely to provide important new insights into human social evolution


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