## 1 Introduction  
Nuclear reaction induced by fast neutrons is pivotal in the development of the fusion reactor. Hence, cross-section data for a neutron-induced nuclear reaction are needed in the design of a nuclear reactor. Plasma facing components are one of the main materials that make up parts of a fusion reactor, such as the first wall and divertor. 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). Thus, 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].  
The nuclear cross-section can usually be determined utilizing empirical measurements, which are available in the public database EXFOR [4]. However, there is a low amount of experimental nuclear cross-sections for some nuclear reactions in certain energy ranges due to experiment complexities. There is also a possible discrepancy between nuclear cross-section data obtained through experimental measurement, especially around 14 MeV incident neutron energy. Thus, theoretical models, such as the preequilibrium excitonmodel [5] and Weisskopf-Ewing theory [6], and systematic analysis of empirical and semi-empirical formulas [7; 8; 9; 10] have been developed to provide measurements for otherwise unavailable nuclear cross-section data.  
In recent years, machine learning algorithms have been very popular with applications spanning a wide range of scientific applications. The main idea behind machine learning algorithms is to unearth the patterns and successfully make predictions. This is done by training a machine learning model with a pre-existing dataset. Random forest algorithm has also been employed in benchmarking the quality of Evaluated Nuclear Data Files (ENDF) [11]. The benchmarking is done by tracing the discrepancy between simulated and experimental effective neutron multiplication factors, \(k_{eff}\). Gaussian process regression has also been applied in generating the nuclear cross-section of proton-induced reactions on the nickel target [12]. The data-driven approach has successfully generated a regression curve along with its corresponding uncertainties which can be used in designing a nuclear experiment with reduced uncertainty.  
Here, we present the study on the prediction of nuclear cross-section data of \({}^{92}\text{Mo}(n,2N)\)\({}^{91}\text{Mo}\) nuclear reaction using machine learning algorithms. However, the availability of experimental data over the wide range of incident neutron energy and the discrepancy of datasets, due to the differences in equipment and experimental methodology limits the predictive capability of machine learning algorithms. We proposed to increase the accuracy of the algorithms by introducing crude estimation of property (CEP) as the input [13]. CEP consists of estimations made from the model and the simulation results of a physical system which is based on the physics of the experiments. Hence, the dataset fed into the machine learning algorithms consists of nuclear cross-sections computed from nuclear code EMPIRE 3.2 [14] and TALYS 1.9 [15], experimental nuclear cross-section and the energy of the incident neutron. The outputs are then compared to the experimental data from EXFOR [4] and evaluated data library ENDF/B-VIII.0 [16].