Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

In recent years, the emergence of novel 2D monoelemental materials (Xenes), eg,
graphdiyne, borophene, phosphorene, antimonene, bismuthene, and stanene, has …

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 …

High-entropy alloy nanoparticles (HEA-NPs) show great potential as functional materials,–.
However, thus far, the realized high-entropy alloys have been restricted to palettes of similar …

Ammonia decomposition on lithium imide surfaces has been intensively investigated owing
to its potential role in a sustainable hydrogen-based economy. Here, through advanced …

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 …

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 …

Understanding the phase behaviour of nanoconfined water films is of fundamental
importance in broad fields of science and engineering. However, the phase behaviour of the …

Machine learning models for the potential energy of multi-atomic systems, such as the deep
potential (DP) model, make molecular simulations with the accuracy of quantum mechanical …

Dynamics has long been recognized to play an important role in heterogeneous catalytic
processes. However, until recently, it has been impossible to study their dynamical behavior …