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 …

Density functional theory calculations are robust tools to explore the mechanical properties
of pristine structures at their ground state but become exceedingly expensive for large …

We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …

We present a stochastic integrated machine learning based multiscale approach for the
prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric …

Recently, single crystalline carbon nitride 2D material with a C 3 N stoichiometry has been
synthesized. In this investigation, we explored the mechanical response and thermal …

Carbon based two-dimensional (2D) materials with honeycomb lattices, like graphene,
polyaniline carbon-nitride (C 3 N) and boron-carbide (BC 3) exhibit exceptional physical …

Graphics processing units have been extensively used to accelerate classical molecular
dynamics simulations. However, there is much less progress on the acceleration of force …

Recent experimental advances for the fabrication of various borophene sheets introduced
new structures with a wide range of applications. Borophene is the boron atom analogue of …

In this study, the structural, electronic and optical properties of theoretically predicted C 6 N
monolayer structure are investigated by means of Density Functional Theory-based First …