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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Physics-informed Machine Learning Framework for Material Parameters Estimation from Materials Morphology
Author(s) Asfandyar Khan, Mahmood Mamivand
On-Site Speaker (Planned) Asfandyar Khan
Abstract Scope Reliable phase-field prediction of microstructure evolution requires parameters that are difficult to measure directly. This work presents a physics-informed machine learning framework for forward simulation and inverse estimation in a split Cahn–Hilliard formulation. The model was benchmarked against the Fe–Cr spinodal decomposition. The network predicts chromium concentration and chemical potential while enforcing the governing PDEs, initial conditions, and periodic concentration boundaries. To improve training for nonlinear phase-field dynamics, the temporal domain is advanced through sequential subdomains with transfer learning. The main focus is inverse modeling, where observed concentration fields are incorporated into the loss to infer the gradient energy coefficient and explore reconstruction of the chemical free-energy derivative. The framework demonstrates how microstructural morphology can be used not only to reproduce spinodal evolution, but also to extract governing phase-field quantities within a single physics-constrained learning problem.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Phase Transformations, Machine Learning

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