The ability to perform nanometer‐scale optical imaging and spectroscopy is key to
deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints …

Data-driven approaches are currently renovating the field of heterogenous catalysis and
open the door to advance catalyst design. Their success depends heavily on the synergy …

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 …

In this paper, we study phase transitions of the q-state Potts model through a number of
unsupervised machine learning techniques, namely Principal Component Analysis (PCA), k …

In this article, we explore the potential of artificial neural networks, which are trained using
an exceptionally simplified catalog of ideal configurations encompassing both order and …

The detection of phase transitions is a fundamental challenge in condensed matter physics,
traditionally addressed through analytical methods and direct numerical simulations. In …

This paper presents the investigation of convolutional neural network (CNN) prediction
successfully recognizing the temperature of the nonequilibrium phase transitions in two …

Both computational and experimental material discovery bring forth the challenge of
exploring multidimensional and often non-differentiable parameter spaces, such as phase …

We construct a deep neural network to enhance the resolution of spin structure images
formed by spontaneous symmetry breaking in the magnetic systems. Through the deep …

Recently, deep generative models using machine intelligence are widely utilized to
investigate scientific systems by generating scientific data. In this study, we experiment with …