Related papers: Nuclear liquid-gas phase transition with machine l…
Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be…
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly…
Recent years have seen a significant increase in the use of machine intelligence for predicting electronic structure, molecular force fields, and the physicochemical properties of various condensed systems. However, substantial challenges…
We investigate the structural similarities between liquid water and 53 ices, including 20 knowncrystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a…
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale…
We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using…
Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial…
We develop a nonperturbative technique in field theory to study properties of infinite nuclear matter at zero temperature as well as at finite temperatures. Here we dress the nuclear matter with off-mass shell pions. The techniques of…
Within the micro-canonical ensemble phase transitions of first order can be identified without invoking the thermodynamic limit. We show for the liquid-gas transition of sodium, potassium, and iron at normal pressure that the transition…
Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and…
In the standard model of particle physics, the strong force is characterized by the theory of quantum chromodynamics (QCD). It is commonly understood from QCD properties that hadrons, at sufficiently high temperatures or densities, melt…
The study of heavy-ion collisions presents a challenge to both theoretical and experimental nuclear physics. Due to the extremely short lifetime and small size of the collision system, disentangling information provided by experimental…
Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
The liquid-gas phase transition is analyzed from the topologic properties of the event distribution in the obervables space. A multi-canonical formalism allows to directly relate the standard phase transition with neutral particles to the…
Nucleation phenomena commonly observed in our every day life are of fundamental, technological and societal importance in many areas, but some of their most intimate mechanisms remain however to be unravelled. Crystal nucleation, the early…
Supercooled water is expected to exhibit a liquid--liquid phase transition between low- and high-density liquid states, possibly terminating in a liquid--liquid critical point in the experimentally difficult no man's land. Because the…
The ability of a feed-forward neural network to learn and classify different states of polymer configurations is systematically explored. Performing numerical experiments, we find that a simple network model can, after adequate training,…
A new theory on gas-liquid phase transition is given. The new idea is that the total intermolecular potential energy for a classical system in equilibrium is relative with the average distance of molecules. A new space homogeneity…
In this work, we present an efficient framework that combines machine learning potential (MLP) and metadynamics to explore multi-dimensional free energy surfaces for investigating solid-solid phase transition. Based on the spectral…