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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title Machine Learning-Driven Multiscale Analysis of Mechanical Properties in Metal-Matrix Nanocomposites
Author(s) Md. Shahrier Hasan, Wenwu Xu
On-Site Speaker (Planned) Wenwu Xu
Abstract Scope We present an innovative multiscale modeling approach for Metal-Matrix Nanocomposites (MMNCs) that leverages Machine Learning (ML) to seamlessly integrate Molecular Dynamics (MD) simulations with the Finite Element Method (FEM). This novel methodology captures the influence of nanoparticle inclusions on the mechanical properties of MMNCs at the macroscale. Our process begins with MD simulations conducted under varied conditions to generate a comprehensive dataset on the mechanical responses of MMNCs. This dataset is utilized to train ML models, which are subsequently employed to enhance FEM analysis. The resulting multiscale model not only accurately predicts the deformation behavior of MMNCs but also aligns with existing experimental and theoretical data, thus validating its efficacy. Our approach offers a scalable solution for advanced composite material analysis, representing a significant advancement in predictive modeling capabilities within materials science.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, ICME

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