# Deep Learning Phase Segregation

arXiv: https://arxiv.org/abs/1803.08993 · HTML: https://mlsanity.com/paper/1803.08993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Accelerated Solutions of Coupled Phase-Field Problems using Generative Adversarial Networks](https://mlsanity.com/paper/2211.12084/) (2022)

## Abstract (arXiv)

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.