Document Type
Article
Publication Date
2024
Publication Title
Journal of Physics B: Atomic, Molecular and Optical Physics
Keywords
cross section, machine learning, electron-impact ionization
Abstract
Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning (ML) algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient ML model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that with training on as few as 10 molecular datasets, the network is able to predict the experimental cross sections of additional molecules with an accuracy similar to experimental uncertainties in existing data. As the number of training molecular datasets increased, the network's predictions became more accurate and, in the worst case, were within 30% of accepted experimental values. In many cases, predictions were within 10% of accepted values. Using a network trained on datasets for 25 different molecules, we present predictions for an additional 27 molecules, including alkanes, alkenes, molecules with ring structures, and DNA nucleotide bases.
Funding Source
We gratefully acknowledge the support of the National Science Foundation under Grant No. PHY-1912093. This article was published Open Access thanks to a transformative agreement between Milner Library and IOP.
DOI
10.1088/1361-6455/ad2185
Recommended Citation
A L Harris and J Nepomuceno 2024 J. Phys. B: At. Mol. Opt. Phys. 57 025201 DOI 10.1088/1361-6455/ad2185.
Comments
First published in Journal of Physics B: Atomic, Molecular and Optical Physics, 57, no. 2. DOI: 10.1088/1361-6455/ad2185.
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.