IKEBANA: Data-Driven Neural-Network Predictor of Electron-Impact K-Shell Ionization Cross Sections

A fully connected neural network was trained to model the K-shell ionization cross sections based on two input features: the atomic number and the incoming electron overvoltage. The training utilized a recent, updated compilation of experimental data covering elements from H to U, and incident elect...

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Bibliografski detalji

Glavni autori:	Darío M. Mitnik, Claudia C. Montanari, Silvina Segui, Silvina P. Limandri, Judith A. Guzmán, Alejo C. Carreras, Jorge C. Trincavelli
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2025-09-01
Serija:	Atoms
Teme:	ionization K-shell machine learning electron impact neural networks
Online pristup:	https://www.mdpi.com/2218-2004/13/9/80
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