相关论文: Ab initio quality neural-network potential for sod…
We propose a generalisation of molecular density functional theory to describe inhomogeneous solvent mixture, with the objective of modelling electrolytic solutions. Two electrolytic models are presented, both within the HNC approximation.…
Large-scale simulations of plastic deformation and phase transformations in alloys require reliable classical interatomic potentials. We construct an embedded-atom method potential for niobium as the first step in alloy potential…
The theoretical exploration of the phase diagrams of binary hydrides under pressure using \emph{ab initio} crystal structure prediction techniques coupled with first-principles calculations has led to the \emph{in silico} discovery of…
Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale…
The current search for room-temperature superconductivity is inspired by the unique properties of the electron-phonon interaction in metal superhydrides. Encouraged by the recently found highest-$T_C$ superconductor fcc-$LaH_{10}$, here we…
The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the…
Hydrogen-rich superhydrides are believed to be very promising high-T$_c$ superconductors, with experimentally observed critical temperatures near room temperature, as shown in recently discovered lanthanide superhydrides at very high…
A first principles machine learning model has been developed aimed at studying the formation of calcium carbonate from aqueous solution using molecular dynamics simulations. The model, dubbed SCAN-ML, reproduces accurately the potential…
Motivated by the very low diffusivity recently found in ab initio simulations of liquid water, we have studied its dependence with temperature, system size, and duration of the simulations. We use ab initio molecular dynamics (AIMD),…
This article embraces a theoretical description of the first order phase transition in liquid metals with application of a cell fluid model. The results are obtained through calculation of the grand partition function without usage of…
Equilibrium physical properties of the solid and liquid phases of parahydrogen, computed by first principle computer simulations, are compared for different choices of pairwise, spherically symmetric intermolecular potentials. The most…
Understanding the structure and chemical composition at the liquid-nanoparticle (NP) interface is crucial for a wide range of physical, chemical and biological processes. In this study, direct imaging of the liquid-NP interface by atom…
Neural network potentials trained on quantum-mechanical data can calculate molecular interactions with relatively high speed and accuracy. However, neural network potentials might exhibit instabilities, nonphysical behavior, or lack…
Titanate nanotubes (TNT) with different sodium contents have been synthesised using a hydrothermal approach and a swift and highly controllable post-washing processes. The influence of the sodium/proton replacement on the structural and…
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress…
Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50…
Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy close to the ab-initio methods used to build them. Besides modeling potential energy surfaces, neural-nets can…
A recently proposed class of machine-learning interatomic potentials --- Moment tensor potentials (MTPs) --- is investigated in this work. MTPs are able to actively select configurations and parametrize the potential on-the-fly. It is shown…
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success of Deep Potential rests on the proper treatment of locality…
While the surface charge of solid particles is a direct consequence of their synthesis, the one of suspended oil drops depends on the adsorption equilibrium of the surrounding molecules. The presence of salt raises the polarity of the water…