Training an artificial neural network for recognizing electron collision patterns

Jaehyun Nam, Hwanmoo Yong, Jungho Hwang, Jongeun Choi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Electron scattering cross sections have been acquired both theoretically and experimentally over the last few decades. By combining scattering data with machine learning, this work is designed to provide physics benefits: AI assisted incorrect-data screening, cross section data generation, and inverse design. As a basic task before undertaking these applications, we present essential training procedures in this paper. We trained electron-collision data to train a neural network with the type of each collision. The neural network with two hidden layer was implemented using multilayer perceptrons, the earliest deep learning model. Monte Carlo cross-validation was employed to ensure the reliability of the test results, and the optimal model structure was obtained by performing Bayesian optimization. We evaluated our model through the performance indicators within a confidence interval. The results indicate that the data were well-learned, except for the attachment class. Furthermore, feature investigation was carried out to ensure the decision-making process.

Original languageEnglish
Article number127005
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume387
DOIs
Publication statusPublished - 2021 Jan 28

Bibliographical note

Funding Information:
This research was supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( 2019M1A2A2104119 ).

Funding Information:
This research was supported by Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2019M1A2A2104119).

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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