## 6 Citations

### Machine Learning Percolation Model

- Computer Science
- 2021

The findings indicate that the effectiveness of machine learning still needs to be evaluated in the applications of phase transitions and critical phenomena.

### Detecting composite orders in layered models via machine learning

- Computer ScienceNew Journal of Physics
- 2020

This work uses machine learning and a suitable quasidistance between different points of the phase diagram to study layered spin models, in which the spin variables constituting each of the uncoupled systems are coupled to each other via an interlayer coupling.

### Unsupervised identification of the phase transition on the 2D-Ising model

- Physics
- 2019

We investigate deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice. We use spin configurations produced for the 2-dimensional…

### The critical temperature of the 2D-Ising model through deep learning autoencoders

- Computer ScienceThe European Physical Journal B
- 2020

It is demonstrated that Tc(L) extrapolates to the known theoretical value as L →∞ suggesting that the autoencoder can also be used to extract the critical temperature of the phase transition to an adequate precision.

### Machine learning methods trained on simple models can predict critical transitions in complex natural systems

- Computer SciencebioRxiv
- 2021

A novel detection method, using simulated outcomes from a range of simple mathematical models with varying nonlinearity to train a deep neural network to detect critical transitions - the Early Warning Signal Network (EWSNet), which can flag a critical transition with unprecedented accuracy.

### Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization

- Computer ScienceTheoretical Chemistry Accounts
- 2021

A convolutional neural network model is proposed for learning and predicting the energy of a system by training geometries of cluster units containing both metal and non-metal atoms, which is later used for generating a huge number of systems required while searching for the stable structure.

## References

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- Computer SciencePhysical Review B
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An analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer, and demonstrates how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks.

### Learning phase transitions by confusion

- Computer Science
- 2017

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 learning of phase transitions: from principal component analysis to variational autoencoders

- Computer SciencePhysical review. E
- 2017

Unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional XY model are examined, finding that the most promising algorithms are principal component analysis and variational autoencoders.

### Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination.

- PhysicsPhysical review. E
- 2017

It is demonstrated that quantified principal components from PCA not only allow the exploration of different phases and symmetry-breaking, but they can distinguish phase-transition types and locate critical points in frustrated models such as the triangular antiferromagnet.

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- Computer Science
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It is shown that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians.

### An exact mapping between the Variational Renormalization Group and Deep Learning

- Computer ScienceArXiv
- 2014

This work constructs an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs), and suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.

### Discovering phase transitions with unsupervised learning

- Computer Science
- 2016

This work shows that unsupervised learning techniques can be readily used to identify phases and phases transitions of many-body systems by using principal component analysis to extract relevant low-dimensional representations of the original data and clustering analysis to identify distinct phases in the feature space.

### Machine learning of frustrated classical spin models. I. Principal component analysis

- Computer Science
- 2017

This work feeds the compute with data generated by the classical Monte Carlo simulation for the XY model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions and shows that the outputs of the principle component analysis agree very well with the understanding of different orders in different phases.

### Machine Learning Phases of Strongly Correlated Fermions

- Physics, Computer SciencePhysical Review X
- 2017

This work shows that a three dimensional convolutional network trained on auxiliary field configurations produced by quantum Monte Carlo simulations of the Hubbard model can correctly predict the magnetic phase diagram of the model at the average density of one (half filling).

### Quantum Loop Topography for Machine Learning.

- PhysicsPhysical review letters
- 2017

This work introduces quantum loop topography (QLT): a procedure of constructing a multidimensional image from the "sample" Hamiltonian or wave function by evaluating two-point operators that form loops at independent Monte Carlo steps, and establishes the first case of obtaining a phase diagram with a topological quantum phase transition with machine learning.