7 research outputs found
Adsorption of common solvent molecules on graphene and MoS 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. 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