Machine learning for phase behavior in active matter systems-Reference-Cited by-同舟云学术

Machine learning for phase behavior in active matter systems

Published:2021 Issue:28 Volume:17 Page:6808-6816
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Dulaney Austin R.¹²³⁴^ORCID,Brady John F.¹²³⁴

Affiliation:

1. Division of Chemistry and Chemical Engineering

2. California Institute of Technology

3. Pasadena

4. USA

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.

Funder

National Science Foundation

Nvidia

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SM/D1SM00266J

Reference32 articles.

1. Athermal Phase Separation of Self-Propelled Particles with No Alignment

2. Towards a thermodynamics of active matter

3. Active Brownian equation of state: metastability and phase coexistence

4. Thermodynamic phases in two-dimensional active matter

5. Generalized thermodynamics of motility-induced phase separation: phase equilibria, Laplace pressure, and change of ensembles

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning assisted sorting of active microswimmers;The Journal of Chemical Physics;2024-09-03

2. Curiosity-driven search for novel nonequilibrium behaviors;Physical Review Research;2024-07-11

3. Machine Learning in Soft Matter: From Simulations to Experiments;Advanced Functional Materials;2024-01-31

4. Machine learning methods for liquid crystal research: phases, textures, defects and physical properties;Soft Matter;2024

5. Spatial distribution order parameter prediction of collective system using graph network;Chinese Physics B;2023-05-01