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Machine learning for phase behavior in active matter systems.

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Deep learning accurately predicts motility-induced phase separation (MIPS) in active Brownian particles (ABPs). This machine learning approach identifies particle phases, aiding in understanding complex phase behavior.

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Area of Science:

  • Physics
  • Soft Matter Physics
  • Computational Physics

Background:

  • Active Brownian particles (ABPs) exhibit complex phase behaviors, including motility-induced phase separation (MIPS).
  • Understanding the phase diagrams of active matter systems is crucial for various applications.
  • Traditional methods for determining phase boundaries can be computationally intensive.

Purpose of the Study:

  • To apply deep learning techniques for predicting MIPS in ABPs.
  • To develop a particle-level phase prediction method using machine learning.
  • To validate the machine learning approach against established simulation data.

Main Methods:

  • Utilized a combination of a fully connected network and a graph neural network.
  • Employed individual particle features to predict particle phase.
  • Calculated the fraction of dilute particles to classify system states (homogeneous dilute, dense, coexistence).

Main Results:

  • Deep learning models accurately predicted particle phases in ABP suspensions.
  • The computed dilute particle fractions aligned well with MIPS binodal curves from simulations.
  • Machine learning effectively identified different phase regions.

Conclusions:

  • Deep learning offers an efficient and accurate method for predicting MIPS in ABPs.
  • This approach can be extended to determine more complex phase diagrams in active matter.
  • Machine learning provides a powerful tool for analyzing the phase behavior of particle systems.