Graphene-reinforced aluminum (Gr/Al) nanocomposites offer exceptional mechanical properties for aerospace, automotive, and electronics applications. Precise estimation of their characteristics, including ultimate tensile strength (UTS) and Young’s modulus (YM), remains challenging due to complex atomic interactions and computational limitations of traditional methods. This study proposes a novel machine learning framework combining Molecular Dynamics (MD) simulations, Adaptive Fast Desensitized Kalman Filter (AFDKF), Diffusion Variational Graph Neural Network (DV-GNN), and Arctic Tern Optimizer (ATO) for efficient and accurate mechanical property prediction. Important variables such as graphene alignment, volume fraction, chirality, and ambient temperature are captured by the method. DV-GNN achieves a prediction accuracy of 99.9%, significantly outperforming existing ML models. The framework also demonstrates low error rates, fast computation, and scalability, providing a robust computational tool for intelligent design of high-strength, lightweight Gr/Al nanocomposites.

Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations
    Abstract
    1 Introduction
    2 Literature survey
    3 Suggested methodology
        3.1 MD simulations
        3.2 Pre-processing using AFDKF
        3.3 Rationale for AFDKF preprocessing
        3.4 Predicting the mechanical properties using DV-GNN
        3.5 Optimization using Arctic Tern optimizer
        3.6 Graphene/Aluminum nanocomposite model and molecular dynamics simulation
    4 Result and discussion
        4.1 Performance measures
            4.1.1 Accuracy
            4.1.2 Precision
            4.1.3 Recall
            4.1.4 F1-score
    4.2 Discussion
    4.3 Risk of data intrusion between the testing and training sets
    4.4 Sensitivity to variations in MD simulation parameters
    4.5 Discussion
    5 Conclusion
    References

• Prasanna Venkatesh Ramdas(Department of Mechanical Engineering, SACS MAVMM Engineering College, Madurai 625301, Tamil Nadu, India)
• Venkatesan Ganapathy(Department of Mechanical Engineering, Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar District 626115, India)
• Anand Palanivel(Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, Tamil Nadu, India)
• Shunmugasundaram Manoharan(Department of Mechanical Engineering, Sri Venkateswaraa College of Technology, Vadakal, Sriperumbudur, Tamil Nadu 602105, India)