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 use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Despite being only one-atom thick, defect-free graphene is considered to be completely
impermeable to all gases and liquids,,,,,,,,–. This conclusion is based on theory,,,,–and …

BACKGROUND Membranes are thin physical barriers that allow for transport of certain
species while limiting others. Atomically thin two-dimensional (2D) materials are crystalline …

Defect-free graphene is impermeable to all atoms,,,–and ions, under ambient conditions.
Experiments that can resolve gas flows of a few atoms per hour through micrometre-sized …

Research on two-dimensional (2D) materials has been explosively increasing in last
seventeen years in varying subjects including condensed matter physics, electronic …

Abstract Machine-learning force fields (MLFF) should be accurate, computationally and data
efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs …

Defect-free monolayers of graphene and hexagonal boron nitride are surprisingly
permeable to thermal protons, despite being completely impenetrable to all gases. It …

Two-dimensional materials represented by graphene are widely used to fabricate
membranes with nanopores or nanochannels and have shown great promise in the field of …

Graphene has recently been shown to be permeable to thermal protons, the nuclei of
hydrogen atoms, which sparked interest in its use as a proton-conducting membrane in …