  Molecular dynamics simulations using empirical force fields (EFFs) are
crucial for gaining fundamental insights into atomic structure and long
timescale dynamics of Au nanoclusters with far-reaching applications in energy
and devices. This approach is thwarted by the failure of currently available
EFFs in describing the size-dependent dimensionality and diverse geometries
exhibited by Au clusters (e.g., planar, hollow cages, pyramids). Owing to their
ability to account for bond directionality, bond-order based EFFs, such as the
Tersoff-type Bond Order Potential (BOP), are well suited for such a
description. Nevertheless, the predictive power of existing BOP parameters is
severely limited in the nm length scale owing to the predominance of bulk Au
properties used to train them. Here, we mitigate this issue by introducing a
new hybrid bond order potential (HyBOP), which account for (a) short-range
interactions via Tersoff-type BOP terms and (b) long-range effects by a scaled
LJ term whose contribution depends on the local atomic density. We optimized
the independent parameters for our HyBOP using a global optimization scheme
driven by genetic algorithms. Moreover, to ensure good transferability of these
parameters across different length scales, we used an extensive training
dataset encompasses structural and energetic properties of a thousand 13-atom
Au clusters, surface energies, as well as bulk polymorphs, obtained from
density functional theory (DFT) calculations. Our newly developed HyBOP has
been found to accurately describe (a) global minimum energy configurations at
different clusters sizes, (b) critical size of transition from planar to
globular clusters, (c) evolution of structural motifs with cluster size, and
(d) thermodynamics, structure, elastic properties of bulk polymorphs as well as
surfaces, in excellent agreement with DFT calculations and spectroscopic
experiments.
