Deep Learning Phase Segregation

Amir Barati Farimani; Joseph Gomes; Rishi Sharma; Franklin L Lee; Vijay S Pande

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Deep Learning Phase Segregation

Amir Barati Farimani, Joseph Gomes, Rishi Sharma, Franklin L Lee and Vijay S Pande

ArXiv.org

03/23/2018

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https://arxiv.org/abs/1803.08993View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

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.

Details

Title: Subtitle: Deep Learning Phase Segregation
Creators: Amir Barati Farimani
Joseph Gomes
Rishi Sharma
Franklin L Lee
Vijay S Pande
Resource Type: Preprint
Publication Details: ArXiv.org
ISSN: 2331-8422
Language: English
Date posted: 03/23/2018
Academic Unit: Chemical and Biochemical Engineering
Record Identifier: 9984209499202771

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