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A probabilistic deep-learning framework that unifies image-based characterization, temporal prediction, and microstructure forecasting for in situ high-temperature confocal laser scanning microscopy data and demonstrates the efficacy of the method on Widmanstatten ferrite evolution in S235 steel across five cooling regimes is introduced.
High-quality predictions of solubility parameters can now be made with minimal data, unlocking the potential for discovering new green solvents.