Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure

TL;DR

This paper presents a machine-learning framework that predicts the evolution of alloy microstructures under elastic fields, accurately matching phase field simulations and capable of extrapolating over longer times and larger domains.

Contribution

The authors develop a unified, scalable ML approach that simultaneously extracts elastic parameters and predicts microstructure evolution, extending capabilities beyond existing methods.

Findings

Accurately predicts microstructure evolution under various misfit conditions.

Demonstrates scalability to larger domains and longer time sequences.

Potential to infer external parameters from experimental videos.

Abstract

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.