PIRSA:23050035

Video URL

https://pirsa.org/23050035

Quantum chemistry methods to study strongly correlated systems – from variational to machine learning approaches

Ghosh, D. (2023). Quantum chemistry methods to study strongly correlated systems – from variational to machine learning approaches. Perimeter Institute for Theoretical Physics. https://pirsa.org/23050035

Ghosh, Debashree. Quantum chemistry methods to study strongly correlated systems – from variational to machine learning approaches. Perimeter Institute for Theoretical Physics, May. 09, 2023, https://pirsa.org/23050035 

          @misc{ scivideos_PIRSA:23050035,
            doi = {10.48660/23050035},
            url = {https://pirsa.org/23050035},
            author = {Ghosh, Debashree},
            keywords = {Other Physics},
            language = {en},
            title = {Quantum chemistry methods to study strongly correlated systems {\textendash} from variational to machine learning approaches},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {may},
            note = {PIRSA:23050035 see, \url{https://scivideos.org/pirsa/23050035}}
          }

Debashree Ghosh Indian Association for the Cultivation of Science

Talk numberPIRSA:23050035
DOI10.48660/23050035
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Polyaromatic hydrocarbons (PAHs) such as acenes have long been studied due to its interesting optical properties and low singlet triplet gaps. Earlier studies have already noticed that use of complete valence active space is imperative to the understanding of its qualitative and quantitative properties. Since complete active space based methods cannot be applied to such large active spaces, we have used density matrix renormalization group (DMRG) based approaches. Further small modification to the PAH topology shows interesting new phases of behaviour in its optical gaps. We have understood the effect of these effects based on spin frustration due to the presence of odd membered rings. In this talk, I will discuss these observations from molecular and model Hamiltonian perspectives.Further developments based on artificial neural network based configuration interaction for strongly correlated systems will also be discussed.5  The similarities between the ANNs and the MPS wavefunctions will be leveraged for 2D systems.

Zoom link:  https://pitp.zoom.us/j/92159136836?pwd=ZFJBcXZ3R3czSUcxcThOci9ueStBZz09