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Foundation Model for Composite Microstructures: Reconstruction, Stiffness, and Nonlinear Behavior Prediction

The rapid advancement of machine learning has unlocked numerous opportunities for materials science, particularly in accelerating the design and analysis of materials. However, a significant challenge lies in the scarcity and high cost of obtaining high-quality materials datasets. While foundation models pre-trained on large datasets have excelled in fields like natural language processing by leveraging latent features through transfer learning, their application in materials science remains limited. Here, we present a foundation model specifically designed for composite materials. Pre-trained on a dataset of short-fiber composites to learn robust latent features, the model accurately predicts homogenized stiffness during transfer learning, even with limited training data. Additionally, our model effectively predicts the material's nonlinear behavior by transferring these learned features to an Interaction-based Material Network, which is a constitutive surrogate model. These results demonstrate the potential of our foundation model to capture complex material behaviors. Our findings validate the feasibility and effectiveness of foundation models in composite materials. We anticipate extending this approach to more complex three-dimensional composite materials, polycrystalline materials, and beyond. Moreover, this framework enables high-accuracy predictions even when experimental data are scarce, paving the way for more efficient and cost-effective materials design and analysis.

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