We propose two machine-learning methods based on neural networks, which we
respectively call the phase-classification method and the temperature-identification method …

We propose a general mechanism to realize nematic superconductivity (SC) and reveal its
exotic vestigial phases in the quasicrystal (QC). Starting from a Penrose-Hubbard model, our …

In this paper, a modified method of anomaly detection using convolutional autoencoders is
employed to predict phase transitions in several statistical mechanical models on a square …

Recently, a colossal magnetoresistance (CMR) was observed in Eu Cd 2 P 2, a compound
that does not fit the conventional mixed-valence paradigm. Instead, experimental evidence …

A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to
study the phase transition of two-dimensional q-state Potts model. With the DANN, we only …

Numerical simulations of the full quantum properties of interacting many-body systems by
means of field-theoretic Monte Carlo techniques are often limited due to a sign problem …

In principle, the probability of configurations, determined by the system's partition function or
wave function, encapsulates essential information about phases and phase transitions …

We reinvestigate the phase transitions of the Ising model on the kagome lattice with
antiferromagnetic nearest-neighbor and ferromagnetic next-nearest-neighbor interactions …

Quantum gas microscopy has developed into a powerful tool to explore strongly correlated
quantum systems. However, discerning phases with topological or off-diagonal long range …

For performing regression tasks involved in various physics problems, enhancing the
precision or equivalently reducing the uncertainty of regression results is undoubtedly one of …