In situ epitaxial engineering of graphene and h-BN lateral heterostructure with a tunable morphology comprising h-BN domains

Graphene and hexagonal boron nitride (h-BN), as typical two-dimensional (2D) materials, have long attracted substantial attention due to their unique properties and promise in a wide range of applications. Although they have a rather large difference in their intrinsic bandgaps, they share a very similar atomic lattice; thus, there is great potential in constructing heterostructures by lateral stitching. Herein, we present the in situ growth of graphene and h-BN lateral heterostructures with tunable morphologies that range from a regular hexagon to highly symmetrical star-like structure on the surface of liquid Cu. The chemical vapor deposition (CVD) method is used, where the growth of the h-BN is demonstrated to be highly templated by the graphene. Furthermore, large-area production of lateral G-h-BN heterostructures at the centimeter scale with uniform orientation is realized by precisely tuning the CVD conditions. We found that the growth of h-BN is determined by the initial graphene and symmetrical features are produced that demonstrate heteroepitaxy. Simulations based on the phase field and density functional theories are carried out to elucidate the growth processes of G-h-BN flakes with various morphologies, and they have a striking consistency with experimental observations. The growth of a lateral G-h-BN heterostructure and an understanding of the growth mechanism can accelerate the construction of various heterostructures based on 2D materials

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields by discussing the hierarchical pathway from generating the dataset of reference calculations to the construction of the machine learning model, and the validation of the physics generated by the model. We will use the symmetrized gradient-domain machine learning (sGDML) framework due to its ability to reconstruct complex high-dimensional potential energy surfaces (PES) with high precision even when using just a few hundreds of molecular conformations for training. The data efficiency of the sGDML model allows using reference atomic forces computed with high-level wave-function-based approaches, such as the gold standard coupled-cluster method with single, double, and perturbative triple excitations (CCSD(T)). We demonstrate that the flexible nature of the sGDML framework captures local and non-local electronic interactions (e.g., H-bonding, lone pairs, steric repulsion, changes in hybridization states (e.g., sp2⇌sp3), n → π∗ interactions, and proton transfer) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML models trained for different molecular structures at different levels of theory (e.g., density functional theory and CCSD(T)) provides empirical evidence that a higher level of theory generates a smoother PES. Additionally, a careful analysis of molecular dynamics simulations yields new qualitative insights into dynamics and vibrational spectroscopy of small molecules close to spectroscopic accuracy