Interpretable and unsupervised phase classification
TL;DR: An unsupervised machine learning method for phase classification is demonstrated which is rendered interpretable via an analytical derivation of its optimal predictions and allows for an automated construction scheme for order parameters.
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Abstract: Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method for phase classification which is rendered interpretable via an analytical derivation of its optimal predictions and allows for an automated construction scheme for order parameters. Based on these findings, we propose and apply an alternative, physically-motivated, data-driven scheme which relies on the difference between mean input features. This mean-based method is computationally cheap and directly interpretable. As an example, we consider the physically rich ground-state phase diagram of the spinless Falicov-Kimball model.
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Citations
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Learning phase transitions by confusion
TL;DR: This work proposes a neural-network approach to finding phase transitions, based on the performance of a neural network after it is trained with data that are deliberately labelled incorrectly, and paves the way to the development of a generic tool for identifying unexplored phase transitions.
Unsupervised machine learning of topological phase transitions from experimental data
Niklas Käming,Anna Dawid,Anna Dawid,Korbinian Kottmann,Maciej Lewenstein,Klaus Sengstock,Alexandre Dauphin,Christof Weitenberg +7 more
TL;DR: In this article, different unsupervised machine learning techniques including anomaly detection and influence functions are applied to experimental data from ultracold atoms to obtain the topological phase diagram of the Haldane model.
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Unsupervised machine learning of topological phase transitions from experimental data
Niklas Käming,Anna Dawid,Anna Dawid,Korbinian Kottmann,Maciej Lewenstein,Klaus Sengstock,Alexandre Dauphin,Christof Weitenberg +7 more
- 14 Jul 2021
TL;DR: In this paper, different unsupervised machine learning techniques including anomaly detection and influence functions are applied to experimental data from ultracold atoms to obtain the topological phase diagram of the Haldane model.
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Replacing Neural Networks by Optimal Analytical Predictors for the Detection of Phase Transitions
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TL;DR: This work derives analytical expressions for the optimal output of three widely used NN-based methods for detecting phase transitions and expects similar analyses to provide a deeper understanding of other classification tasks in condensed matter physics.
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•Journal Article
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TL;DR: This work employs an artificial neural network and deep-learning techniques to identify quantum phase transitions from single-shot experimental momentum-space density images of ultracold quantum gases and obtains results that were not feasible with conventional methods.
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