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

CLAUDIA MONTANARI; JESICA PERALTA; ALEJANDRA MENDEZ; FELIPE BIVORT-HAIEK,; DARÍO MITNIK; PARASKEVI DIMITRIOU

Toyama

Congreso; 26th International Conference on Ion Beam Analysis y 18th International Conference on Particle Induced X-ray Emission; 2023

IBA&PIXE

Stopping power cross-sections are relevant to a widerange of applications, such as ion beam analysis,deposition ranges, ion implantation, and radiationdamage, to name a few. Reliable stopping power valuesare also needed in isotope production for medicalapplications, fusion technologies, and detectordevelopments.In this presentation, we will discuss the state of the art ofstopping power knowledge from three differentperspectives: i) The experimental knowledge based onthe most important collection of data, started by H. Pauland now continued by the IAEA [1], recentlymodernized. ii) Full theoretical models that can describethe total stopping (valence electrons up to boundelectrons deeply) for multielectronic targets (Z=57-83,including the lanthanides series) [2,3]. iii) The machinelearning algorithms on the IAEA database to predictaccurate electronic stopping power cross sections for anyion and target combination in a wide range of incidentenergies by the ESPNN (electronic stopping powerneural network) code [4], and the comparison with dataand SRIM predictions.Figure