116 research outputs found
Rich Ground State Chemical Ordering in Nanoparticles: Exact Solution of a Model for Ag-Au Clusters
We show that nanoparticles can have very rich ground state chemical order.
This is illustrated by determining the chemical ordering of Ag-Au 309-atom
Mackay icosahedral nanoparticles. The energy of the nanoparticles is described
using a cluster expansion model, and a Mixed Integer Programming (MIP) approach
is used to find the exact ground state configurations for all stoichiometries.
The chemical ordering varies widely between the different stoichiometries, and
display a rich zoo of structures with non-trivial ordering.Comment: Revised version. New figure added, discussion expanded, some material
moved into supplementary fil
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Neural message passing on molecular graphs is one of the most promising
methods for predicting formation energy and other properties of molecules and
materials. In this work we extend the neural message passing model with an edge
update network which allows the information exchanged between atoms to depend
on the hidden state of the receiving atom. We benchmark the proposed model on
three publicly available datasets (QM9, The Materials Project and OQMD) and
show that the proposed model yields superior prediction of formation energies
and other properties on all three datasets in comparison with the best
published results. Furthermore we investigate different methods for
constructing the graph used to represent crystalline structures and we find
that using a graph based on K-nearest neighbors achieves better prediction
accuracy than using maximum distance cutoff or the Voronoi tessellation graph
Atomic structure optimization with machine-learning enabled interpolation between chemical elements
We introduce a computational method for global optimization of structure and
ordering in atomic systems. The method relies on interpolation between chemical
elements, which is incorporated in a machine learning structural fingerprint.
The method is based on Bayesian optimization with Gaussian processes and is
applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with
CO adsorption, and Cu-Ni clusters. The method consistently identifies
low-energy structures, which are likely to be the global minima of the energy.
For the investigated systems with 23-66 atoms, the number of required energy
and force calculations is in the range 3-75
Benchmark density functional theory calculations for nano-scale conductance
We present a set of benchmark calculations for the Kohn-Sham elastic
transmission function of five representative single-molecule junctions. The
transmission functions are calculated using two different density functional
theory (DFT) methods, namely an ultrasoft pseudopotential plane wave code in
combination with maximally localized Wannier functions, and the norm-conserving
pseudopotential code Siesta which applies an atomic orbital basis set. For all
systems we find that the Siesta transmission functions converge toward the
plane-wave result as the Siesta basis is enlarged. Overall, we find that an
atomic basis with double-zeta and polarization is sufficient (and in some cases
even necessary) to ensure quantitative agreement with the plane-wave
calculation. We observe a systematic down shift of the Siesta transmission
functions relative to the plane-wave results. The effect diminishes as the
atomic orbital basis is enlarged, however, the convergence can be rather slow.Comment: 10 pages, 7 figure
Band Gap Tuning and Defect Tolerance of Atomically Thin Two- Dimensional Organic-Inorganic Halide Perovskites
Organic–inorganic
halide perovskites have proven highly
successful for photovoltaics but suffer from low stability, which
deteriorates their performance over time. Recent experiments have
demonstrated that low dimensional phases of the hybrid perovskites
may exhibit improved stability. Here we report first-principles calculations
for isolated monolayers of the organometallic halide perovskites (C<sub>4</sub>H<sub>9</sub>NH<sub>3</sub>)<sub>2</sub>MX<sub>2</sub>Y<sub>2</sub>, where M = Pb, Ge, Sn and X,Y = Cl, Br, I. The band gaps
computed using the GLLB-SC functional are found to be in excellent
agreement with experimental photoluminescence data for the already
synthesized perovskites. Finally, we study the effect of different
defects on the band structure. We find that the most common defects
only introduce shallow or no states in the band gap, indicating that
these atomically thin 2D perovskites are likely to be defect tolerant
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