相关论文: Modeling liquid water by climbing up Jacob's ladde…

We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory…

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the…

We introduce a coarse-grained deep neural network model (CG-DNN) for liquid water that utilizes 50 rotational and translational invariant coordinates, and is trained exclusively against energies of ~30,000 bulk water configurations. Our…

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H$_2$O) and heavy water (D$_2$O) within the isothermal-isobaric ensemble. In particular, the deep neural network is…

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here we utilize the Deep Potential methodology -- a machine learning approach -- to study this…

Kohn-Sham density functional theory (DFT) is a widely-used electronic structure theory for materials as well as molecules. DFT is needed especially for large systems, ab initio molecular dynamics, and high-throughput searches for functional…

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a…

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…

Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately…

Density functional simulations of condensed phase water are typically inaccurate, due to the inaccuracies of approximate functionals. A recent breakthrough showed that the SCAN approximation can yield chemical accuracy for pure water in all…

Co$_3$O$_4$ is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of Co$_3$O$_4$-water interfaces under operando conditions remains extremely challenging. While ab…

In molecular simulations, neural network force fields aim at achieving \emph{ab initio} accuracy with reduced computational cost. This work introduces enhancements to the Deep Potential network architecture, integrating a message-passing…

We perform ab initio molecular dynamics (AIMD) simulation of liquid water in the canonical ensemble at ambient conditions using the SCAN meta-GGA functional approximation, and carry out systematic comparisons with the results obtained from…

An accurate prediction of the surface potential at the air-water interface is critical to calculating ion hydration free energies and electrochemical half-cell potentials. Using Density Functional Theory (DFT), model interfacial…

Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catalysis to battery technology. Density functional theory (DFT)…

In the last decade, computational studies of liquid water have mostly concentrated on ground state properties. However recent spectroscopic measurements have been used to infer the structure of water, and the interpretation of optical and…

Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in both storage and computation, which has restricted the…

Computational chemistry is a powerful tool for the discovery of novel materials. In particular, it is used to simulate ionic liquids in search of electrolytes for electrochemical applications. Herein, the choice of the computational method…

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…

Liquid water is fundamentally important, and its accurate computer simulation has been the driving force for myriad methodological developments. Ab initio molecular dynamics with forces obtained from density functional theory (DFT) is now a…