Molecular dynamics simulation and machine learning models for predicting welding and tensile properties of diffusion-welded Aluminum-Nickel

In this study, machine learning (ML) models are developed to predict the value of interfacial region thickness (IRT) and ultimate tensile strength (UTS) of diffusion-bonded Al-Ni based on molecular dynamics simulation data. Molecular dynamics simulations are performed to simulate the diffusion bonding of Al-Ni with three parameters with three to four level for each parameter. The results of the simulations that are used to generate the ML models are the value of IRT and UTS. The temperature have influenced the performance of ML models with significant impact, indicated by the value of MSE and R 2 that used temperature as the input parameters with an excellent performance. However, the combination of the three parameters as the input shows the best performance, indicated by the MSE value of 0 and the R 2 value of 1, showing that the ML models