Neutron-Induced Nuclear Cross-Sections Study for Plasma Facing Materials via Machine Learning: Molybdenum Isotopes

Mohamad Amin Bin Hamid,Yusuff Afeez Oluwatobi,Saba Ayub,Xiao Yan Chew,Hoe Guan Beh

doi:10.3390/app11167359

Mohamad Amin Bin Hamid, Yusuff Afeez Oluwatobi + Show 3 more

Open Access

https://doi.org/10.3390/app11167359

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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 of 92Mo 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.

Highlights

Nuclear reaction induced by fast neutrons is pivotal in the development of the fusion reactor
There are three main components to the input to feed into our algorithms, which are the incident energy of the neutron, En, experimental cross-section data (EXP) and the computation cross-section, which consists of various output from EMPIRE 3.2 and TALYS 1.9 nuclear code done in the previous study [3,16,17,18,19,20]
By using Recursive Feature Elimination (RFE), we can decrease the number of features used while increasing the performance of our machine learning model which can be seen in Tables A1 and A2

Summary

Introduction

Nuclear reaction induced by fast neutrons is pivotal in the development of the fusion reactor. Cross-section data for a neutron-induced nuclear reaction are needed in the design of a nuclear reactor. The plasma-facing components are the first to be exposed to plasma generated from the D-T reaction in the reactor, which is heavily bombarded by fast neutrons (around 14 MeV). The materials used to fabricate plasma-facing components must be able to withstand the high neutron flux and are usually made from beryllium [1], tungsten [2], and molybdenum [3]. There is a possible discrepancy between nuclear cross-section data obtained through experimental measurement, especially around 14 MeV incident neutron energy. Theoretical models, such as the preequilibrium exciton

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