Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure
This work addresses the challenge of modeling alloy microstructures for materials science, offering a generalizable framework that could be applied to experimental data, though it is incremental as it builds on existing neural network architectures.
The authors tackled the problem of predicting elastic parameters and simulating microstructure evolution in strained alloys by introducing a unified machine-learning framework, achieving accurate predictions of misfit conditions and full microstructure evolution under a wide spectrum of conditions, with demonstrated scalability to larger domains and mild extrapolation errors for time sequences five times longer than training samples.
We introduce a unified machine-learning framework designed to conveniently tackle the temporal evolution of alloy microstructures under the influence of an elastic field. This approach allows for the simultaneous extraction of elastic parameters from a short trajectory and for the prediction of further microstructure evolution under their influence. This is demonstrated by focusing on spinodal decomposition in the presence of a lattice mismatch eta, and by carrying out an extensive comparison between the ground-truth evolution supplied by phase field simulations and the predictions of suitable convolutional recurrent neural network architectures. The two tasks may then be performed subsequently into a cascade framework. Under a wide spectrum of misfit conditions, the here-presented cascade model accurately predicts eta and the full corresponding microstructure evolution, also when approaching critical conditions for spinodal decomposition. Scalability to larger computational domain sizes and mild extrapolation errors in time (for time sequences five times longer than the sampled ones during training) are demonstrated. The proposed framework is general and can be applied beyond the specific, prototypical system considered here as an example. Intriguingly, experimental videos could be used to infer unknown external parameters, prior to simulating further temporal evolution.