Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a
growing interest has been developed in the replacement of empirical interatomic potentials …

This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

In this work, we comprehensively study the mechanical properties of the newly synthesized
monolayer quasi-hexagonal-phase fullerene (qHPF) membrane [Hou et al., 2022] under …

Metal–organic frameworks (MOFs) are a family of materials that have high porosity and
structural tunability and hold great potential in various applications, many of which require a …

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 …

Simulating collision cascades and radiation damage poses a long-standing challenge for
existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning …

Two-dimensional fullerene networks have been synthesized in several forms, and it is
unknown which monolayer form is stable under ambient conditions. Using first-principles …

Graphitic carbon nitride () as a kind of important 2D materials, shows great potential
applications as electric and photoelectric devices. However, most reported works mainly …

Abstract Pd-Cu-Ni-P alloy is an ideal model system of metallic glass known for its
exceptional glass-forming ability. However, few correlation of structures with properties was …

Abstract Two-dimensional (2D) PbTe monolayers as newly fabricated thermoelectric
materials have sparked great interest due to their excellent physical properties, which are …