eprintid: 14590
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/45/90
datestamp: 2023-11-10 03:29:10
lastmod: 2023-11-10 03:29:10
status_changed: 2023-11-10 01:57:18
type: article
metadata_visibility: show
creators_name: Hamid, M.A.B.
creators_name: Beh, H.G.
creators_name: Oluwatobi, Y.A.
creators_name: Chew, X.Y.
creators_name: Ayub, S.
title: Neutron-induced nuclear cross-sections study for plasma facing materials via machine learning: Molybdenum isotopes
ispublished: pub
note: cited By 3
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
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112530113&doi=10.3390%2fapp11167359&partnerID=40&md5=9a5a71c316b4521649fe8d97da31dc97
id_number: 10.3390/app11167359
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 11
number: 16
refereed: TRUE
issn: 20763417
citation:   Hamid, M.A.B. and Beh, H.G. and Oluwatobi, Y.A. and Chew, X.Y. and Ayub, S.  (2021) Neutron-induced nuclear cross-sections study for plasma facing materials via machine learning: Molybdenum isotopes.  Applied Sciences (Switzerland), 11 (16).   ISSN 20763417