To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

Machine learning (ML) models for molecules and materials commonly rely on a
decomposition of the global target quantity into local, atom-centered contributions. This …

Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …

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 …

We propose an approach that can accurately predict the heat conductivity of liquid water. On
the one hand, we develop an accurate machine-learned potential based on the …

Over the last decade, an increasing body of evidence has emerged, supporting the
existence of a metastable liquid–liquid critical point in supercooled water whereby two …

In this paper, we investigate the performance of different machine learning potentials (MLPs)
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …

Despite governing heat management in any realistic device, the microscopic mechanisms of
heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based …