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primary topic: Neutron-induced nuclear cross-sections study for plasma facing materials via machine learning: Molybdenum isotopes

Neutron-induced nuclear cross-sections study for plasma facing materials via machine learning: Molybdenum isotopes
type: AcademicArticle
type: Article
abstract: In this work, we apply a machine learning algorithm to the regression analysis of the nuclear cross-section of neutron-induced nuclear reactions of molybdenum isotopes,92Mo at incident neutron energy around 14 MeV. The machine learning algorithms used in this work are the Random Forest (RF), Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The performance of each algorithm is determined and compared by evaluating the root mean square error (RMSE) and the correlation coefficient (R2). We demonstrate that machine learning can produce a better regression curve of the nuclear cross-section for the neutron-induced nuclear reaction of92Mo isotopes compared to the simulation results using EMPIRE 3.2 and TALYS 1.9 from the previous literature. From our study, GPR is found to be better compared to RF and SVM algorithms, with R2 = 1 and RMSE = 0.33557. We also employed the crude estimation of property (CEP) as inputs, which consist of simulation nuclear cross-section from TALYS 1.9 and EMPIRE 3.2 nuclear code alongside the experimental data obtained from EXFOR (1 April 2021). Although the Experimental only (EXP) dataset generates a more accurate cross-section, the use of CEP-only data is found to generate an accurate enough regression curve which indicates a potential use in training machine learning models for the nuclear reaction that is unavailable in EXFOR. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2021
volume: 11
issue: 16
is part of: Applied Sciences (Switzerland)
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

MDPI AG
type: Organization

Applied Sciences (Switzerland)
type: Collection
issn: 20763417

M.A.B. Hamid
type: Person
given name: M.A.B.
family name: Hamid

H.G. Beh
type: Person
given name: H.G.
family name: Beh

Y.A. Oluwatobi
type: Person
given name: Y.A.
family name: Oluwatobi

X.Y. Chew
type: Person
given name: X.Y.
family name: Chew

S. Ayub
type: Person
given name: S.
family name: Ayub

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primary topic: Neutron-induced nuclear cross-sections study for plasma facing materials via machine learning: Molybdenum isotopes