Genetic Programming Approach for Electron-Alkali-Metal Atom Collisions

Abstract

New technique is presented for modeling the total cross sections of electron scattering by Na, K, Rb and Cs atoms in the low and intermediate energy regions. The calculations have been performed in the framework of genetic programming ( GP ) technique. The GP has been running based on the experimental data of the total collisional cross sections to produce the total cross sections for each target atom. The incident energy and atomic number as well as the static dipole polarizability have been used as input variables to find the functions that describe the total collisional cross sections of the scattering of electrons by alkali atoms. The experimental, calculated and predicted total collisional cross sections are compared. The discovered functions show a good match to the experimental data.

Total cross section prediction of the collisions of positrons and electrons with alkali atoms using Gradient Tree Boosting

“…We notice that our GTB model calculations give an improvement upon the theoretical models. Also, in comparison with our previous work (Genetic Programming model [15,16]) we have found that the GTB model gives best fitting in very short computational time (0.73 sec).…”

“…Recently, there are a number of machine learning techniques such as neural networks and genetic programming [13][14][15][16][17][18][19][20][21][22] used to predict and model the total cross sections. The reason for applying machine learning methods (based on statistical learning theory or biological inspired techniques, etc.)…”