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 …

Over the past few decades, molecular dynamics simulations and first-principles calculations
have become two major approaches to predict the lattice thermal conductivity (κ L), which …

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 …

The spontaneously formed passivation layer, the solid electrolyte interphase (SEI) between
the electrode and electrolyte, is crucial to the performance and durability of Li ion batteries …

Molten salts are important thermal conductors used in molten salt reactors and solar
applications. To use molten salts safely, accurate knowledge of their thermophysical …

Molten MgCl2-based chlorides have emerged as potential thermal storage and heat transfer
materials due to high thermal stabilities and lower costs. In this work, deep potential …

Two-dimensional (2D) materials have intriguing physical and chemical properties, which
exhibit promising applications in the fields of electronics, optoelectronics, as well as energy …

LiF–NaF–ZrF4 multicomponent molten salts are promising candidate coolants for advanced
clean energy systems owing to their desirable thermophysical and transport properties …

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 …

We developed a deep potential machine learning model for simulations of chemical
reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2, using …