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Continuous-mixture Autoregressive Networks for the Efficient Variational Calculation of Spin System

Lingxiao Wang, Yin Jiang, Lianyi He, Kai Zhou

Published May 11, 2020• 0 citations
OpenAlex

Abstract

We develop the deep autoregressive networks with multi-channel to compute continuous spin system directly. As a concrete example, the 2-D XY model is embedded into the continuous-mixture networks, in which the Kosterlitz-Thouless phase transition emerges on a periodic square lattice. Vortices as the quasi-Long Range Order(LRO) are accurately detected by the autoregressive neural networks. By learning the microscopic possibility distributions from macroscopic thermal distribution, the free energy is directly calculated and vortices (anti-vortices) pair emerge with temperature rise. As a more precise estimation, the helicity modulus is evaluated to anchor the transition temperature to be at $\beta_\text{c}=1.101$. Although the time-consuming of the training process inevitably increases with the lattice size, the training time remains unchanged around the transition temperature.

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We develop the deep autoregressive networks with multi-channel to compute continuous spin system directly. As a concrete example, the 2-D XY model is embedded into the continuous-mixture networks, in which the Kosterlitz-Thouless phase transition emerges on a periodic square lattice. Vortices as the quasi-Long Range Order(LRO) are accurately detected by the autoregressive neural networks. By learning the microscopic possibility distributions from macroscopic thermal distribution, the free energy is directly calculated and vortices (anti-vortices) pair emerge with temperature rise. As a more precise estimation, the helicity modulus is evaluated to anchor the transition temperature to be at βc=1.101\beta_\text{c}=1.101βc​=1.101. Although the time-consuming of the training process inevitably increases with the lattice size, the training time remains unchanged around the transition temperature.
A generative model, the Variational Autoregressive Networks(VANs) is introduced to recognize the Kosterlitz-Thouless phase transition on a periodic square lattice. Vortices as the quasi-Long Range Order(LRO) are accurately detected by the ordering neural networks. By learning the microscopic possibility distributions from macroscopic thermal distribution, the free energy is directly calculated and vortices(anti-vortices) pair emerge with temperature rising. As a more precise estimation, the helicity modulus is evaluated to anchor the transition temperature to be at βc=1.101\beta_\text{c}=1.101βc​=1.101. Although time-consuming of the training process is inevitably increasing with the lattice size, the training time remains unchanged around the transition temperature.
We develop deep autoregressive networks with multi channels to compute many-body systems with continuous spin degrees of freedom directly. As a concrete example, we demonstrate the two-dimensional XY model with the continuous-mixture networks and rediscover the Kosterlitz–Thouless (KT) phase transition on a periodic square lattice. Vortices characterizing the quasi-long range order are accurately detected by the generative model. By learning the microscopic probability distributions from the macroscopic thermal distribution, the networks are trained as an efficient physical sampler which can approximate the free energy and estimate thermodynamic observables unbiasedly with importance sampling. As a more precise evaluation, we compute the helicity modulus to determine the KT transition temperature. Although the training process becomes more time-consuming with larger lattice sizes, the training time remains unchanged around the KT transition temperature. The continuous-mixture autoregressive networks we developed thus can be potentially used to study other many-body systems with continuous degrees of freedom.
We present a transformer-based variational autoregressive network for sampling lattice systems with continuous degrees of freedom and apply it to the two-dimensional XY model near the Berezinskii-Kosterlitz-Thouless transition. The architecture is combined with physics-informed ingredients: locality-informed attention and approximate probabilities. The resulting sampler significantly outperforms existing neural samplers. We train the model on lattices up to 96×9696\times9696×96, verify with a mode-dropping estimator that no parts of the configuration space are missed, and reproduce the energy, magnetization and helicity modulus obtained with the traditional Markov Chain Monte Carlo algorithm.
We propose a general framework to extract microscopic interactions from raw configurations with deep neural networks. The approach replaces the modeling Hamiltonian by the neural networks, in which the interaction is encoded. It can be trained with data collected from Ab initio computations or experiments. The well-trained neural networks give an accurate estimation of the possibility distribution of the configurations at fixed external parameters. It can be spontaneously extrapolated to detect the phase structures since classical statistical mechanics as prior knowledge here. We apply the approach to a 2D spin system, training at a fixed temperature, and reproducing the phase structure. Scaling the configuration on lattice exhibits the interaction changes with the degree of freedom, which can be naturally applied to the experimental measurements. Our approach bridges the gap between the real configurations and the microscopic dynamics with an autoregressive neural network.
In this paper, we apply machine learning methods to study phase transitions in certain statistical mechanical models on the two-dimensional lattices, whose transitions involve nonlocal or topological properties, including site and bond percolations, the XY model, and the generalized XY model. We find that using just one hidden layer in a fully connected neural network, the percolation transition can be learned and the data collapse by using the average output layer gives correct estimate of the critical exponent ν. We also study the Berezinskii-Kosterlitz-Thouless transition, which involves binding and unbinding of topological defects, vortices and antivortices, in the classical XY model. The generalized XY model contains richer phases, such as the nematic phase, the paramagnetic and the quasi-long-range ferromagnetic phases, and we also apply machine learning method to it. We obtain a consistent phase diagram from the network trained with only data along the temperature axis at two particular parameter Δ values, where Δ is the relative weight of pure XY coupling. Aside from using the spin configurations (either angles or spin components) as the input information in a convolutional neural network, we devise a feature engineering approach using the histograms of the spin orientations in order to train the network to learn the three phases in the generalized XY model and demonstrate that it indeed works. The trained network by using system size L×L can be used to the phase diagram for other sizes (L^{'}×L^{'}, where L^{'}≠L) without any further training.
We propose two machine-learning methods based on neural networks, which we respectively call the phase-classification method and the temperature-identification method, for detecting different types of phase transitions in the XXZ models without prior knowledge of their critical temperatures. The XXZ models have exchange couplings which are anisotropic in the spin space where the strength is represented by a parameter $\mathrm{\ensuremath{\Delta}}(>0)$. The models exhibit second-order phase transition when $\mathrm{\ensuremath{\Delta}}>1$, but the Berezinskii-Kosterlitz-Thouless (BKT) phase transition when $\mathrm{\ensuremath{\Delta}}<1$. In the phase-classification method, the neural network is trained using spin or vortex configurations of well-known classical spin models other than the XXZ models, e.g., the Ising models and the XY models, to classify those of the XXZ models to corresponding phases. We demonstrate that the trained neural network successfully detects the phase transitions for both $\mathrm{\ensuremath{\Delta}}>1$ and $\mathrm{\ensuremath{\Delta}}<1$, and the evaluated critical temperatures coincide well with those evaluated by conventional numerical calculations. In the temperature-identification method, on the other hand, the neural network is trained so as to identify temperatures at which the input spin or vortex configurations are generated by the Monte Carlo thermalization. The critical temperatures are evaluated by analyzing the optimized weight matrix, which coincides with the result of numerical calculation for the second-order phase transition in the Ising-like XXZ model with $\mathrm{\ensuremath{\Delta}}=1.05$ but cannot be determined uniquely for the BKT transition in the XY-like XXZ model with $\mathrm{\ensuremath{\Delta}}=0.95$.