Available and open-source database is a big challenge in using artificial intelligence in martial science, particularly for microstructure processing.

On the other hand, the integration of big data and ML in materials science has greatly increased our understanding of materials and has opened up new avenues for research and innovation. Therefore, a key objective of computational materials design has been comprehending and forecasting microstructure evolution. One of the pillars of contemporary materials research is the ability to manage the evolution of the materials’ microstructure while it is being processed or used, including common phenomena like solidification, solid-state phase transitions, and grain growth. The process-structure–property relationships of engineered materials are directly impacted by material microstructures, which are mesoscale structural elements that operate as an essential link between atomistic building components and macroscopic qualities. The results show that the trained network predicts quantitatively accurate microstructure morphologies while it is several orders of magnitude faster than the phase field method. As a case study, we used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method for training and predicting future microstructures by previous observations. We propose a Predictive Recurrent Neural Network (PredRNN) model for the microstructure prediction, which extends the inner-layer transition function of memory states in LSTMs to spatiotemporal memory flow. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. Therefore, they are not practical when either there is an urgent need for microstructure morphology during the process or there is a need to generate big microstructure datasets. Simulation tools for microstructure evolution prediction based on physical concepts are computationally expensive and time-consuming.

Prediction of microstructure evolution during material processing is essential to control the material properties.

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