Adsorption of common solvent molecules on graphene and MoS2_2 from first-principles

Solvents are an essential element in the production and processing of two-dimensional (2D) materials. For example, the liquid phase exfoliation of layered materials requires a solvent to prevent the resulting monolayers from re-aggregating, while solutions of functional atoms and molecules are routinely used to modify the properties of the layers. It is generally assumed that these solvents do not interact strongly with the layer and so their effects can be neglected. Yet experimental evidence has suggested that explicit atomic-scale interactions between the solvent and layered material may play a crucial role in exfoliation and cause unintended electronic changes in the layer. Little is known about the precise nature of the interaction between the solvent molecules and the 2D layer. Here, we use density functional theory calculations to determine the adsorption configuration and binding energy of a variety of common solvent molecules, both polar and non-polar, on two of the most popular 2D materials, namely graphene and MoS$_2$. We show that these molecules are physisorbed on the surface with negligible charge transferred between them. We find that the adsorption strength of the different molecules is independent of the polar nature of the solvent. However, we show the molecules induce a significant charge rearrangement at the interface after adsorption as a result of polar bonds in the molecule.Comment: 8 pages, 6 figure

The role of solvent interfacial structural ordering in maintaining stable graphene dispersions

Liquid phase exfoliation (LPE) is the most promising method for the low-cost, scalable production of two-dimensional nanosheets from their bulk counterparts. Extensive exfoliation occurs in most solvents due to the huge amount of energy introduced by sonication or shear mixing. However, the subsequent dispersion is not always stable, with extensive reaggregation occurring in some solvents. Identifying the optimal solvent for a particular layered material is difficult and requires a fundamental understanding of the mechanism involved in maintaining a stable dispersion. Here, we use molecular dynamics calculations to show that when graphene is immersed in a solvent, distinct solvation layers are formed irrespective of the choice of solvent and their formation is energetically favourable for all considered solvents. However, energetic considerations such as these do not explain the experimental solvent-dependence of the dispersion concentration. Instead, we find that solvents with high diffusion coefficients parallel to the graphene layer result in the lowest experimental concentration of graphene in solution. This can be explained by the enhanced ease of reaggregation in these solvents. Solvents with smaller diffusion coefficients result in higher experimental graphene concentrations as reaggregation is prevented. In the low diffusion limit, however, this relationship breaks down. We suggest that here the concentration of graphene in solution depends primarily on the separation efficiency of the initial exfoliation step. Based on this, we predict that the concentration of exfoliated graphene in solvents such as benzaldehyde and quinoline, which have low diffusion constants, can be increased dramatically by careful tuning of the experimental sonication parameters

Cluster expansion constructed over Jacobi-Legendre polynomials for accurate force fields

We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The constituent many-body contributions are separated, interpretable and adaptable to replicate the physical knowledge of the system. In fact, the flexibility introduced by the use of the Jacobi polynomials allows us to impose, in a natural way, constrains and symmetries to the cluster expansion. This has the effect of reducing the number of parameters needed for the fit and of enforcing desired behaviours of the potential. For instance, we show that our Jacobi-Legendre cluster expansion can be designed to generate potentials with a repulsive tail at short inter-atomic distances, without the need of imposing any external function. Our method is here continuously compared with available machine-learning potential schemes, such as the atomic cluster expansion and potentials built over the bispectrum. As an example we construct a Jacobi-Legendre potential for carbon, by training a slim and accurate model capable of describing crystalline graphite and diamond, as well as liquid and amorphous elemental carbon.Comment: 16 Pages, 8 figures, 6 Page supplementary materia

Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations

Abstract Kohn–Sham density functional theory (KS-DFT) is a powerful method to obtain key materials’ properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme