Related papers: Nuclear liquid-gas phase transition with machine l…
As powerful as machine learning (ML) techniques are in solving problems involving data with large dimensionality, explaining the results from the fitted parameters remains a challenging task of utmost importance, especially in physics…
Machine learning techniques have been shown to be effective to recognize different phases of matter and produce phase diagrams in the parameter space interested, while they usually require prior labeled data to perform well. Here, we…
The present manuscript gives a theoretical description of the first-order phase transition in a cell fluid model with a modified Morse potential and additional repulsive interaction. In the framework of the grand canonical ensemble, the…
In this paper we present a review on our recent experimental investigations into the phase behavior of the deeply cooled water confined in a nanoporous silica material, MCM-41, with elastic neutron scattering technique. Under such strong…
We use a simplified model which is based on the same physics as inherent in most statistical models for nuclear multifragmentation. The simplified model allows exact calculations for thermodynamic properties of systems of large number of…
We study the exciton gas-liquid transition in GaAs/AlGaAs coupled quantum wells. Below a critical temperature, Tc=4.8K, and above a threshold laser power density the system undergoes a phase transition into a liquid state. We determine the…
We investigate the role of the Coulomb interaction on the liquid-gas phase transition of nuclear matter with three different values of proton fraction ($Y_p$=0.5,0.3 and 0.1), relevant for heavy-ion physics as well as various astrophysical…
We study the high temperature transition in pure $SU(3)$ gauge theory and in full QCD with 3D-convolutional neural networks trained as parts of either unsupervised or semi-supervised learning problems. Pure gauge configurations are obtained…
Understanding the process of multiphase fluid flow through porous media is crucial for many climate change mitigation technologies, including CO$_2$ geological storage, hydrogen storage, and fuel cells. However, current numerical models are…
The percolation study offers valuable insights into the characteristics of phase transition, shedding light on the underlying mechanisms that govern the formation of global connectivity within the system. We explore the percolation phase…
We present an analytical description of the phase transitions from a nucleon gas to nuclear matter and from nuclear matter to quark matter within the same model. The equation of state for quark and nuclear matter is encoded in the effective…
Experimental nuclear level densities at excitation energies below the neutron threshold follow closely a constant-temperature shape. This dependence is unexpected and poorly understood. In this work, a fundamental explanation of the…
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate…
The liquid-gas phase transition in strange hadronic matter is reexamined by using the new parameters about the $\Lambda - \Lambda$ interaction deduced from recent observation of $^{6}_{\Lambda\Lambda}He$ double hypernucleus. The extended…
Contemporary work implies generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. In this Letter, we utilize a score-based modeling procedure called Thermodynamic…
We propose an iterative proposal to estimate critical points for statistical models based on configurations by combing machine-learning tools. Firstly, phase scenarios and preliminary boundaries of phases are obtained by…
Recently, the learning by confusion (LBC) approach has been proposed as a machine learning tool to determine the critical temperature Tc of phase transitions without any prior knowledge of its even approximate value. However, the…
Known for their ability to identify hidden patterns in data, artificial neural networks are among the most powerful machine learning tools. Most notably, neural networks have played a central role in identifying states of matter and phase…
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…