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 language | English |
|---|---|
| Article number | 105002 |
| Journal | Journal of Physics B: Atomic, Molecular and Optical Physics |
| Volume | 52 |
| Issue number | 10 |
| DOIs | |
| State | Published - Apr 24 2019 |
Keywords
- genetic algorithm
- machine learning
- potential energy curves
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