Active Learning Assisted MCCI to Target Spin States in Model Hamiltonian Systems

13 September 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Strongly correlated systems and their accurate solutions have been challenging to quantum chemistry. Several methods have been developed over the years for the accurate understanding of such systems, and selected configuration interaction and Monte Carlo configuration interaction (MCCI) form important classes of systems in this category. However, MCCI is plagued by slow convergence. This is further exacerbated by the fact that most of the current MCCI implementations do not target specific spin states. In our work, we use active learning assisted MCCI to speed up the convergence manyfold and also develop a method for spin targeting. This method has been tested with several model Hamiltonian systems akin to molecular systems and has shown improved convergence and accuracy.

Keywords

Active learning
Monte Carlo CI
Model Hamiltonian
Spin state

Supplementary materials

Title
Description
Actions
Title
Supporting Information: Active Learning Assisted MCCI to Target Spin States in Model Hamiltonian Systems
Description
Supplementary material describes: 1. Structure of Artificial Neural Network (ANN) 2. Targeting the Spin States
Actions

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