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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Determination of Phase-Field Model Parameters Using Machine Learning Approach
Author(s) Benjamin Rhoads, Shailee Yagnik, Samrat Choudhury
On-Site Speaker (Planned) Benjamin Rhoads
Abstract Scope Phase-field approach has been previously employed to investigate the microstructure evolution during a variety of solid-solid phase transformations. However, developing a phase-field model often requires a wide range of material parameters, which are typically obtained either from experimental measurements or simulations at lower length and/or time scales. In this project, we present an alternative approach based on optimization algorithm to determine the parameter(s) needed to develop a phase-field model. Optimization is performed by comparing phase-field generated microstructures with experimental microstructures under similar conditions. We demonstrate the validity of our machine learning algorithm using microstructures of Ni-Al alloys during coarsening of gamma prime precipitates as a model system. We will show that our machine learning algorithm is generic in nature and can be potentially applied to expedite the development of a wide range of phase-field models.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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