Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation

Abstract

In this study, we constructed a dataset of elastic modulus and ultimate stress for copper material enhanced by Transition Metal Dichalcogenides (TMDs) through Molecular Dynamics (MD) simulations. Subsequently, leveraging chemical insights, we selected appropriate descriptors and established machine learning prediction models for elastic modulus and ultimate stress, respectively. Finally, the performance of the machine learning models was evaluated using a test set. The results demonstrate excellent performance of the machine learning models in predicting material properties. This work presents a novel approach for efficient material screening, demonstrating the synergy between MD simulations and machine learning in advancing materials research and intelligent material selection platforms.