Stopping power in matter, from the IAEA database to theoretical and machine learning predictions

C.C. MONTANARI; J. PERALTA; A. MENDEZ; F. BIVORT HAIEK; D.M. MITNIK; P. DIMITRIOU

Toyama

Conferencia; 26th International Conference on Ion Beam Analysis y 18th International Conference on Particle Induced X-ray Emission (IBA-PIXE2023); 2023

IBA&PIXE

In this presentation we will discuss the state of art of thestopping power knowledge with three different perspectives: i) The experimental knowledge based on the most important collection of data, started by H. Paul and now continued by the IAEA, recently modernized. ii) Full theoretical models that can describe the total stopping (valence electrons up to deeply bound electrons) for multielectronic targets (Z=57-83, including the lamthanide serie), iii) The machine learning algorithms on the IAEA database to predict accurate electronic stopping power cross sections for any ion andtarget combination in a wide range of incident energies, by the ESPNN (electronic stopping power neural-network) code.neural-network ) code [4], and the comparison with dataand SRIM predictions.