Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

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 …

For 35 years, ab initio molecular dynamics (AIMD) has been the method of choice for
modeling complex atomistic phenomena from first principles. However, most AIMD …

Batteries are vital energy storage carriers in industry and in our daily life. There is continued
interest in the developments of batteries with excellent service performance and safety …

We present the GPU version of DeePMD-kit, which, upon training a deep neural network
model using ab initio data, can drive extremely large-scale molecular dynamics (MD) …

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 …

Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-
generation Li-ion batteries. Molecular dynamics could provide atomic scale information to …

We compute the thermal conductivity of water within linear response theory from equilibrium
molecular dynamics simulations, by adopting two different approaches. In one, the potential …

Materials for solid-state batteries often exhibit complex chemical compositions, defects, and
disorder, making both experimental characterization and direct modeling with first principles …

Atomistic simulations have become an invaluable tool for industrial applications ranging
from the optimization of protein-ligand interactions for drug discovery to the design of new …