Deep Learning Phase Segregation
This work addresses phase segregation prediction for chemical, mechanical, and biological systems, representing an incremental improvement through a data-driven approach.
The authors tackled the problem of predicting phase segregation in binary mixtures by developing a deep learning model that directly maps initial fluid dispersion to equilibrium concentration fields, achieving up to 98% accuracy in reproducing key physical properties like area, perimeter, and free energy distributions.
Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for the learning, modeling, and prediction of phase segregation. A direct mapping between an initially dispersed, immiscible binary fluid and the equilibrium concentration field is learned by conditional generative convolutional neural networks. Concentration field predictions by the deep learning model conserve phase fraction, correctly predict phase transition, and reproduce area, perimeter, and total free energy distributions up to 98% accuracy.