End-to-End Machine Learning for Experimental Physics: Using Simulated Data to Train a Neural Network for Object Detection in Video Microscopy

Minor, Eric N.; Howard, Stian D.; Green, Adam A. S.; Park, Cheol S.; Clark, Noel A.

doi:10.48550/arxiv.1908.05271

Cited by 1 publication

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“…In experiments, liquid crystals are usually studied using optical imaging [29]. Recently Machine learning techniques are applied to classify phases and predict the physical properties of liquid crystals from the experimental data [30][31][32]. Convolutional neural networks (CNNs) have been shown to successfully classify nematic and isotropic phases in continuum with very high accuracy [33].…”

Section: Introductionmentioning

confidence: 99%

Machine Learned Phase Transitions in a System of Anisotropic Particles on a Square Lattice

Padavala¹,

Singh²,

Kundu³

2021

Preprint

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The area of Machine learning (ML) has seen exceptional growth in recent years. Successful implementation of ML methods in various branches of physics has led to new insights. These methods have been shown to classify phases in condensed matter systems. Here we study the classification problem of phases in a system of hard rigid rods on a square lattice around a continuous and a discontinuous phase transition using supervised learning (with prior knowledge about the transition points). On comparing a number of ML models we find that convolutional neural network (CNN) classifies the phases with the highest accuracy when only snapshots are given as inputs. We study how the system size affects the model performance. We compare the performance of CNN in classifying the phases around a continuous and a discontinuous phase transition. Further, we show that one can even beat the accuracy of CNN with simpler models by using physics-guided features. Lastly, we show that the critical point in this system can be learned without any prior estimate by using only the information of the ordered phase (as training set). Our study reveals the ML techniques that have been successful in studying spin systems can be easily adapted to more complex systems.

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