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Genetic based fitting techniques for high precision potential energy curves of diatomic molecules

  • Purdue University

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

We present development of a genetic algorithm for fitting potential energy curves of diatomic molecules to experimental data. Our approach does not involve any functional form for fitting, which makes it a general fitting procedure. In particular, it takes in a 'trial' potential, along with experimental measurements of vibrational binding energies, rotational constants, and their experimental uncertainties. The fitting procedure is able to converge to better than 1% uncertainty, as measured by X2 or reproduce the experimental data to better than 0.03 cm?1. We present the details of this technique for the X 1+ of lithium'rubidium.

Original languageEnglish
Article number105002
JournalJournal of Physics B: Atomic, Molecular and Optical Physics
Volume52
Issue number10
DOIs
StatePublished - Apr 24 2019

Keywords

  • genetic algorithm
  • machine learning
  • potential energy curves

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