Related papers: Principal deuterium Hugoniot via Quantum Monte Car…

Quantum molecular dynamic (QMD) simulations are introduced to study the thermophysical properties of liquid deuterium under shock compression. The principal Hugoniot is determined from the equation of states, where contributions from…

We present Coupled Electron-Ion Monte Carlo results for the principal Hugoniot of deuterium together with an accurate study of the initial reference state of shock wave experiments. We discuss the influence of nuclear quantum effects,…

We have performed accurate simulations of the Deuterium Hugoniot using Coupled Electron Ion Monte Carlo (CEIMC). Using highly accurate quantum Monte Carlo methods for the electrons, we study the region of maximum compression along the…

We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP). We use kernel regression in combination with SOAP (Smooth Overlap…

Two first-principles simulation techniques, path integral Monte Carlo (PIMC) and density functional molecular dynamics (DFT-MD), are applied to study hot, dense helium in the density-temperature range of 0.387 - 5.35 g/cc and 500 K -…

We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate…

Restricted path integral Monte Carlo simulations have been used to calculate the equilibrium properties of deuterium for two densities: 0.674 and 0.838 gcm^-3 (rs = 2.00 and 1.86) in the temperature range of 10000 < T < 1000000 K. Using the…

Path integral Monte Carlo simulations have been used to study deuterium at high pressure and temperature. The equation of state has been derived in the temperature and density region of 10000 < T < 1000000 K and 0.6 < rho < 2.5 gcm-3. A…

The Hugoniot for Deuterium is calculated using the classical-map hyper-netted-chain (CHNC) approach using several models of the effective temperature that may be assigned to the electron-ion interaction. This effective temperature embodies…

The equation of state and the shock Hugoniot of deuterium are calculated using a first-principles approach, for the conditions of the recent shock experiments. We use density functional theory within a classical mapping of the quantum…

We report ab initio calculations of the melting curve and Hugoniot of molybdenum for the pressure range 0-400 GPa, using density functional theory (DFT) in the projector augmented wave (PAW) implementation. We use the ``reference…

The properties of hot dense helium at megabar pressures were studied with two first-principles computer simulation techniques, path integral Monte Carlo and density functional molecular dynamics. The simulations predicted that the…

We present computational results and tables of the equation-of-state, thermodynamic properties, and shock Hugoniot for hot dense fluid deuterium. The present results are generated using a recently developed chemical model that takes into…

Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework employing deep neural networks to systematically predict the…

Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD…

In order to provide a comprehensive theoretical description of MgSiO$_3$ at extreme conditions, we combine results from path integral Monte Carlo (PIMC) and density functional molecular dynamics simulations (DFT-MD) and generate a…

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and…

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…

Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust…

We propose a theoretical Hugoniot obtained by combining results for the equation of state (EOS) from the Direct Path Integral Monte Carlo technique (DPIMC) and those from Reaction Ensemble Monte Carlo (REMC) simulations. The main idea of…