Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/678
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dc.contributor.authorDatta, Sumita-
dc.date.accessioned2023-05-22T05:44:29Z-
dc.date.available2023-05-22T05:44:29Z-
dc.date.issued2022-09-26-
dc.identifier.urihttps://doi.org/10.1142/S0217979223500248-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/678-
dc.description.abstractIn this paper, an importance sampling method based on the Generalized Feynman–Kac (GFK) method has been used to calculate the mean values of quantum observables from quantum correlation functions for many-body systems with the Born–Oppenheimer approximation in the nonrelativistic limit both at zero and finite temperature. Specifically, the expectation values ⟨rni⟩ , ⟨rnij⟩ , ⟨r−ni⟩ and ⟨r−nij⟩ for the ground state of the lithium and beryllium and the density matrix, the partition function, the internal energy and the specific heat of a system of quantum harmonic oscillators are computed, in good agreement with the best nonrelativistic values for these quantities. Although the initial results are encouraging, more experimentation will be needed to improve the other existing numerical results beyond chemical accuracies specially for the last two properties for lithium and beryllium. Also more work needs to be done to improve the trial functions for finite temperature calculations. Although these results look promising, more work needs to be done to achieve the spectroscopic accuracy at zero temperature and to estimate the finite temperature effects from the non-Born–Oppenheimer calculations. Also more experimentation will be needed to study the convergence criteria for the inverse properties for atoms at zero temperature.en_US
dc.language.isoenen_US
dc.publisherWorld Scientificen_US
dc.subjectWiener Measureen_US
dc.subjectGeneralized Feynman–Kac methoden_US
dc.subjectQuantum Correlation Functionen_US
dc.subjectPartition Functionen_US
dc.titleComputing quantum correlation functions by importance Sampling method based on path integralsen_US
dc.typeArticleen_US
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