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Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

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Published on: September 23, 2021

Detecting depinning and nonequilibrium transitions with unsupervised machine learning.

D McDermott^1,2, C J O Reichhardt¹, C Reichhardt¹

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

Physical Review. E

|May 20, 2020

Summary

This summary is machine-generated.

Machine learning identifies complex dynamics in driven systems. A novel order-parameter-like measure detects depinning transitions and flow phases, outperforming traditional methods in disordered particle systems.

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Area of Science:

Physics
Complex Systems
Machine Learning

Background:

Systems driven far from equilibrium exhibit complex behaviors.
Depinning transitions and nonequilibrium phase transitions are crucial in various physical systems.
Traditional methods struggle to distinguish subtle dynamic flow phases.

Purpose of the Study:

To develop and validate a machine learning-generated order-parameter-like measure.
To detect depinning transitions and dynamic flow phases in driven particle systems.
To compare the efficacy of the ML measure against traditional methods.

Main Methods:

Numerical simulations of a model disk system.
Application of a machine learning-derived order-parameter-like measure.
Analysis of monodisperse passive disks with short-range interactions driven over quenched disorder.

Main Results:

The ML measure successfully detects depinning transitions.
It distinguishes between flowing liquid and phase-separated liquid-solid states.
The measure reveals distinct behavior in high-density jamming regimes.
It outperforms velocity-force curves and Voronoi tessellation for certain transitions.

Conclusions:

Machine learning offers a powerful tool for analyzing complex nonequilibrium systems.
The developed order-parameter-like measure provides novel insights into dynamic flow phases.
This approach is broadly applicable to particle-based systems with depinning and nonequilibrium phase transitions.