Related papers: Nuclear liquid-gas phase transition with machine l…
Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments, astrophysical observations, and nuclear theory calculations. Advancing our understanding of the nuclear equation of state (EOS,…
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally-excited…
The phase transition of the two-dimensional $U(1)$ quantum link model on the triangular lattice is investigated by employing a supervised neural network (NN) consisting of only one input layer, one hidden layer of two neurons, and one…
Liquid-liquid phase transition of hydrogen is at the center of hydrogen phase diagram as a promising route towards emergent properties such as the Wigner-Huntington metallization, superconductivity, and superfluidity. Here we report a study…
Liquid metals play a central role in new generation liquid metal cooled nuclear reactors, for which numerical investigations require the use of appropriate thermal turbulence models for low Prandtl number fluids. Given the limitations of…
We explore the geometrical structure of Liquid-gas (LG) mixed phase which is relevant to nuclear matter in the crust region of compact stars or supernovae. To get the equation of state (EOS) of the system, the Maxwell construction is found…
We propose and explore a new finite temperature phase of translationally invariant multi-component liquids which we call a "Quantum Disentangled Liquid" (QDL) phase. We contemplate the possibility that in fluids consisting of two (or more)…
We investigate the compression of nuclear matter in relativistic hydrodynamics. Nuclear matter is described by a $\sigma-\omega$--type model for the hadron matter phase and by the MIT bag model for the quark--gluon plasma, with a first…
It has been proposed that multifragmentation can be related to the liquid-gas phase transition of nuclear matter. We study the statistical properties of finite nuclear matter near the phase transition with the help of a Lattice Gas Model…
Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior…
Machine learning has emerged as a promising approach to study the properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods$-$such as Monte Carlo$-$which…
Modelling the sudden depressurisation of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet usually demonstrates a wide range of structures, including…
We present 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. Such shallow neural networks were…
We combine machine learning (ML) with Monte Carlo (MC) simulations to study the crystal nucleation process. Using ML, we evaluate the canonical partition function of the system over the range of densities and temperatures spanned during…
Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale…
We have adapted a set of classification algorithms, also known as Machine Learning, to the identification of fluid and gel domains close to the main transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic molecular…
A three-dimensional numerical simulation is conducted for a complex process in a laser-material system, which involves heat and mass transfer in a compressible gaseous phase and chemical reaction during laser irradiation on a urethane paint…
Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with…
Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits…
Whether the glass transition is caused by an underlying singularity or is a purely kinetic phenomenon is a significant outstanding question. Studying an atomistic glass former, we introduce a sampling method to access temperatures…