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Machine Learning for Phase Behavior in Active Matter Systems

Dulaney, Austin R. and Brady, John F. (2020) Machine Learning for Phase Behavior in Active Matter Systems. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210106-130209799

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Abstract

We demonstrate that deep learning techniques can be used to predict motility induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level. Using a fully connected network in conjunction with a graph neural network we use individual particle features to predict to which phase a particle belongs. From this, we are able to compute the fraction of dilute particles to determine if the system is in the homogeneous dilute, dense, or coexistence region. Our predictions are compared against the MIPS binodal computed from simulation. The strong agreement between the two suggests that machine learning provides an effective way to determine the phase behavior of ABPs and could prove useful for determining more complex phase diagrams.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2011.09458arXivDiscussion Paper
ORCID:
AuthorORCID
Dulaney, Austin R.0000-0002-2428-8913
Brady, John F.0000-0001-5817-9128
Additional Information:A.R.D. would like to thank Yisong Yue for thoughtful discussions pertaining to graph neural networks. J.F.B. acknowledges support by the National Science Foundation under Grant No. CBET-1803662. We gratefully acknowledge the support of the NVIDIA Corporation for the donation of the Titan V GPU used to carry out this work. DATA AVAILABILITY. The data that support the findings of this study are available from the corresponding author upon reasonable request.
Funders:
Funding AgencyGrant Number
NSFCBET-1803662
Record Number:CaltechAUTHORS:20210106-130209799
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210106-130209799
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107347
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Jan 2021 21:29
Last Modified:06 Jan 2021 21:29

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