Extended pseudo-spectral physics-informed neural networks for phase-field models
Using AI to reverse-engineer the hidden rules that drive material separation
Researchers developed a machine-learning method that can figure out the underlying physical laws of phase separation—the process where mixtures split into distinct regions—by watching how materials evolve over time. The technique recovers unknown physical parameters from just a single or handful of snapshot pairs, and continues to work reasonably well even when data is noisy.
Materials scientists often can't directly measure the fundamental properties that control how materials separate and form patterns, but need to know them to design alloys, polymers, and other engineered materials. This method cuts the number of observations needed to infer those hidden rules, potentially speeding up materials discovery and making it cheaper to characterize new substances without running expensive, lengthy experiments.